Content recommendation method, community content recommendation method, commodity content recommendation method

Abstract

The embodiment of the specification provides a content recommendation method, a community content recommendation method and a commodity content recommendation method, wherein the content recommendation method comprises the following steps: obtaining a historical semantic identification sequence of a target user, wherein the historical semantic identification sequence comprises semantic identifications of the target user in a plurality of historical time periods; inputting the historical semantic identification sequence into a pre-trained prediction model to output a semantic identification probability distribution in a future time period; determining a predicted semantic identification based on the semantic identification probability distribution, and determining an interest migration direction of the target user according to a semantic correlation between the predicted semantic identification and the semantic identifications in the plurality of historical time periods; and recommending target recommended content corresponding to the interest migration direction to the target user. The interest prediction and the recommendation task are decoupled, the predicted content supply of the future interest state is realized, the effective time window of the recommendation strategy is expanded, and therefore the interest migration of the user is more accurately predicted, and the quality and the recommendation effect of the recommended content are improved.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of content recommendation technology, and in particular to a content recommendation method, a community content recommendation method, and a product content recommendation method. Background Technology

[0002] With the rapid development of internet content distribution technology, recommendation systems have become the core infrastructure connecting users with massive content services, and accurate modeling and dynamic prediction of user interests have become key technological directions for improving recommendation efficiency.

[0003] Currently, mainstream recommendation systems primarily employ two types of user interest modeling methods. One is an end-to-end recommendation method based on next item prediction, which uses deep neural networks to encode the user's historical item interaction sequences and directly predict the specific product or content the user might interact with next, thus coupling interest modeling and item recommendation within the same model. The other is an offline profiling method based on behavioral tags, which constructs static interest tag profiles by statistically analyzing user historical clicks, add-to-cart, and favorites data for downstream audience segmentation and content targeting.

[0004] However, both of these approaches have shortcomings in practical applications. The next-item prediction-based approach couples interest modeling with item recommendation, failing to independently output an explicit description of the user's interest state. Recommendation intervention can only be achieved through adjusting the weights of globally ranked items, lacking the ability to accurately correspond to changes in the user's own interests. The behavior tag-based approach can only statistically summarize the user's historical interest states, lacking time-series prediction capabilities, failing to reflect the dynamic migration trends of user interests, and unable to make pre-adjustments. Therefore, there is an urgent need for a content recommendation method that offers more accurate interest prediction and higher-quality recommended content. Summary of the Invention

[0005] In view of this, embodiments of this specification provide a content recommendation method. One or more embodiments of this specification also relate to a community content recommendation method, a product content recommendation method, a computing device, a computer-readable storage medium, and a computer program product, to solve the aforementioned technical problems.

[0006] According to a first aspect of the embodiments of this specification, a content recommendation method is provided, comprising: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; Input the historical semantic identifier sequence into the pre-trained prediction model, and output the semantic identifier probability distribution in the future time period; Based on the probability distribution of semantic identifiers, predictive semantic identifiers are determined, and based on the semantic association between the predicted semantic identifiers and semantic identifiers in multiple historical time periods, the direction of interest migration of target users is determined. Recommend targeted content to target users that aligns with their interest migration direction.

[0007] According to a second aspect of the embodiments of this specification, a community content recommendation method is provided, comprising: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; Input the historical semantic identifier sequence into the pre-trained prediction model, and output the semantic identifier probability distribution in the future time period; Based on the probability distribution of semantic identifiers, predictive semantic identifiers are determined, and based on the semantic association between the predicted semantic identifiers and semantic identifiers in multiple historical time periods, the direction of interest migration of target users is determined. Recommend targeted community content to target users that aligns with their interest migration direction.

[0008] According to a third aspect of the embodiments of this specification, a product content recommendation method is provided, including: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; Input the historical semantic identifier sequence into the pre-trained prediction model, and output the semantic identifier probability distribution in the future time period; Based on the probability distribution of semantic identifiers, predictive semantic identifiers are determined, and based on the semantic association between the predicted semantic identifiers and semantic identifiers in multiple historical time periods, the direction of interest migration of target users is determined. Recommend targeted product content that aligns with the direction of interest migration to the target user.

[0009] According to a fourth aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above method.

[0010] According to a fifth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions that, when executed by a processor, implement the steps of the above-described method.

[0011] According to a sixth aspect of the embodiments of this specification, a computer program product is provided, including computer-executable instructions that, when executed by a processor, implement the steps of the above-described method.

[0012] One embodiment of this specification implements a content recommendation method, comprising: obtaining a historical semantic identifier sequence of a target user, wherein the historical semantic identifier sequence includes semantic identifiers of the target user in multiple historical time periods; inputting the historical semantic identifier sequence into a pre-trained prediction model, and outputting a semantic identifier probability distribution in future time periods; determining predicted semantic identifiers based on the semantic identifier probability distribution, and determining the target user's interest migration direction based on the semantic association between the predicted semantic identifiers and the semantic identifiers in multiple historical time periods; and recommending target content to the target user that corresponds to the interest migration direction.

[0013] By acquiring semantic identifiers of target users across multiple historical time periods to construct a historical semantic identifier sequence, and using these discretized semantic identifiers as the basic unit for interest modeling, the interest prediction task is decoupled from the main recommendation task, possessing independent optimization objectives and evaluation dimensions. By inputting the historical semantic identifier sequence into a pre-trained prediction model, the model outputs a probability distribution of semantic identifiers for future time periods, quantifying the uncertainty of users' future interest states in a probabilistic manner and providing a confidence basis for subsequent decisions. By determining predicted semantic identifiers based on the semantic identifier probability distribution and determining the direction of interest migration based on the semantic association between the predicted semantic identifiers and those within the historical time periods, the prediction results at the discrete encoding level are transformed into a description of interest changes with business semantics, making the recommendation intervention logic interpretable. By recommending target content corresponding to the direction of interest migration to target users, the system achieves predicted content supply based on predicted future interest states rather than current states, expanding the effective time window of the recommendation strategy, thereby more accurately predicting users' interest migration and improving the quality and effectiveness of recommended content. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a content recommendation method provided in one embodiment of this specification; Figure 2 This is a flowchart illustrating a clustering codebook update method provided in one embodiment of this specification; Figure 3 This is a flowchart illustrating a community content recommendation method provided in one embodiment of this specification; Figure 4 This is a flowchart illustrating a product content recommendation method provided in one embodiment of this specification; Figure 5 This is a schematic diagram of the structure of a content recommendation device provided in one embodiment of this specification; Figure 6This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0015] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0016] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0017] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0018] Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in one or more embodiments of this specification are obtained through open-source datasets or public datasets that comply with their license agreements, or are obtained with full authorization from the relevant parties. Moreover, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0019] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0020] Residual quantization is a multi-level progressive quantization method that discretizes a continuous vector into a combination of multi-level encoded values ​​by progressively clustering and encoding the residual between the input vector and the quantization result of the previous level.

[0021] A deep neural network (DNN) is an artificial neural network containing multiple hidden layers that performs hierarchical feature extraction and representation learning on input data through layer-by-layer nonlinear transformations.

[0022] The Softmax normalization function is a mathematical function that transforms any real vector into a probability distribution. It achieves this by performing exponential operations on each element of the vector and dividing by the sum of all exponents, ensuring that the output value satisfies the probability constraint that it is non-negative and that the sum of all exponents is one.

[0023] Kernel density estimation (KDE) is a non-parametric probability density estimation method that estimates the continuous probability density distribution of data in space by placing kernel functions around data points and summing them.

[0024] Autoregressive is a sequence modeling method that uses the output of previous time steps as the input condition for subsequent time steps to gradually generate the prediction result of the next element in the sequence.

[0025] Stream computing is a computing paradigm that processes continuously generated data streams in real time. Data is processed one by one or in batches as soon as it is generated, without waiting for the full accumulation of data.

[0026] A Markov chain is a stochastic process model based on state transition probabilities. Its core assumption is that the state at the next time step depends only on the state at the current time step and is independent of earlier historical states.

[0027] Temperature scaling is a method to smooth out model output scores. It controls the sharpness or flatness of the probability distribution by dividing by a temperature parameter. The lower the temperature, the more concentrated the distribution is on high-scoring items.

[0028] Nearest Neighbor Matching is a classification or retrieval method based on distance metrics. It calculates the distance between the object to be matched and all reference objects, and determines the nearest reference object as the matching result.

[0029] With the rapid development of internet content distribution technology, recommendation systems have become the core infrastructure connecting users with massive content services, and accurate modeling and dynamic prediction of user interests have become key technological directions for improving recommendation efficiency.

[0030] Currently, mainstream recommendation systems primarily employ two types of user interest modeling methods. One is an end-to-end recommendation method based on next item prediction, which uses deep neural networks to encode the user's historical item interaction sequences and directly predict the specific product or content the user might interact with next, thus coupling interest modeling and item recommendation within the same model. The other is an offline profiling method based on behavioral tags, which constructs static interest tag profiles by statistically analyzing user historical clicks, add-to-cart, and favorites data for downstream audience segmentation and content targeting.

[0031] However, both of these approaches have shortcomings in practical applications. The next-item prediction-based approach couples interest modeling with item recommendation, failing to independently output an explicit description of the user's interest state. Recommendation intervention can only be achieved through adjusting the weights of globally ranked items, lacking the ability to accurately correspond to changes in the user's own interests. The behavior tag-based approach can only statistically summarize the user's historical interest states, lacking time-series prediction capabilities, failing to reflect the dynamic migration trends of user interests, and unable to achieve pre-adjustment.

[0032] In view of this, this specification provides a content recommendation method, and also relates to a content recommendation device, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0033] Specifically, see Figure 1 , Figure 1 A flowchart of a content recommendation method according to an embodiment of this specification is shown, such as... Figure 1 As shown, the method specifically includes the following steps.

[0034] Step 102: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods.

[0035] The content recommendation method provided in one or more embodiments of this specification can be applied to various recommendation system scenarios that require personalized content distribution based on user interests. Specifically, the content recommendation method provided in the embodiments of this specification can be applied to various content distribution scenarios such as e-commerce transaction recommendation, community content recommendation, short video recommendation, news recommendation, and live streaming recommendation, as well as to combined recommendation scenarios of the above multiple scenarios. The executing entity of this content recommendation method can be the server-side device of the recommendation system, a terminal device with data processing capabilities, or a recommendation system composed of server-side devices and terminal devices working together; the embodiments of this specification do not limit this.

[0036] Target users are active user entities in the recommendation system that need to receive content recommendations. Specifically, target users can include various types of content consumers, such as shoppers on e-commerce platforms, browsers on content platforms, and readers on news platforms. Target users can be any registered user on the platform with historical behavioral data who is currently active; their activity level and interests are not subject to specific restrictions. Furthermore, target users are located at the user entity layer in the recommendation system architecture, and their scope can be flexibly determined according to specific business needs. It can cover all active users on the platform or be limited to a subset of users who meet specific activity criteria.

[0037] Semantic identifiers are multi-layered discrete identifiers generated for each user, uniquely corresponding to a user cluster with explicit business semantics in the user representation space. Specifically, semantic identifiers can include multiple levels of encoded values, each level corresponding to a semantic division of different granularities in the user representation space. The combination of encoded values ​​from multiple levels forms a sequential identifier structure with complex semantics. Semantic identifiers can be obtained by encoding multi-dimensional user behavior representations using deep neural networks and then discretizing them layer by layer through residual quantization, where each quantization layer captures semantic features at different levels of abstraction. Semantic identifiers differ from traditional numerical codes or random hash identifiers; their core feature is that each identifier value carries interpretable business semantic information and contains rich implicit interest features. Furthermore, user clusters corresponding to different semantic identifiers have a natural ability to stratify user value, enabling recommendation systems to distinguish user groups at different value levels based on semantic identifiers, providing a quantitative basis for refined operations.

[0038] A historical semantic identifier sequence is an ordered record of a target user's semantic identifiers across multiple consecutive historical time periods, arranged chronologically. Specifically, a historical semantic identifier sequence can include all semantic identifier states of the target user from an earlier historical period to the current moment, with each element in the sequence corresponding to the user cluster identifier to which the target user belongs within the relevant historical time period. The historical semantic identifier sequence can serve as input data for a prediction model, providing the model with complete trajectory information on the evolution of user interest states over time. Furthermore, the core value of the historical semantic identifier sequence lies in its recording of the static distribution of user interests while simultaneously capturing the dynamic evolution of interest states. By observing the transitions or maintenance patterns between adjacent semantic identifiers in the sequence, it is possible to identify migration signals of user interests, providing crucial temporal feature input for subsequent interest prediction, transforming the user from an abstract entity into a computable sequence of interest states.

[0039] Historical time periods are the basic time units that constitute historical semantic identifier sequences. Each historical time period corresponds to an independent observation record of the semantic identifier state of the target user. Specifically, historical time periods can include equal-length windows divided according to preset time intervals. The end of each window corresponds to a sampling time of the user's interest state. Multiple historical time periods are arranged sequentially on the time axis to form a uniform observation grid. Historical time periods can be flexibly configured according to the platform's business characteristics and prediction accuracy requirements. Different time granularities can be adopted in different business scenarios. Historical time periods can serve as the basic sampling unit for sequence construction, and their division method directly affects the richness of temporal information carried by the sequence. Furthermore, the setting of historical time periods needs to balance information granularity and computational efficiency. Too short a time interval may introduce too much noise interference, while too long a time interval may lose valuable short-term interest fluctuation information. Reasonable division of historical time periods can ensure that the sequence contains sufficient temporal information for pattern recognition while maintaining an appropriate data scale to support efficient model computation.

[0040] In practical applications, obtaining the historical semantic identifier sequence of a target user can be achieved through various methods.

[0041] One alternative approach is to extract the target user's behavior records across various historical time periods from the platform's existing user behavior log system. A pre-deployed semantic encoding model is then used to map these behavior records to corresponding semantic identifiers, and these identifiers are organized into an ordered sequence for storage in chronological order. This method fully utilizes the platform's existing data storage infrastructure, eliminating the need for additional data acquisition components. Sequence construction can be completed simply by adding a semantic encoding step to the existing log architecture, offering advantages such as low implementation cost and good data consistency.

[0042] Another alternative approach is to continuously monitor the target user's behavioral event stream using a real-time streaming computing framework. At the end of each historical time period, a semantic encoding computation task is triggered, and the calculated semantic identifiers are written to a time-series database. When a prediction task is needed, the historical semantic identifier sequences of the target user are read in batches from the time-series database in chronological order. This approach is suitable for business scenarios with high data timeliness requirements, ensuring that the sequence data remains synchronized with the user's latest state, reducing prediction bias caused by data latency, and improving the overall response speed of the recommendation process.

[0043] In addition, an incremental update strategy can be used to maintain the historical semantic identifier sequence of the target user. Only the incremental change of the user behavior representation relative to the previous period is calculated in each new historical period. Based on the incremental change, the existing semantic identifier is locally updated or confirmed, avoiding repeated encoding and calculation of the entire historical data. This can effectively reduce the consumption of computing resources in large-scale user scenarios.

[0044] In this step, by acquiring the historical semantic identifier sequence of the target user, the user's interest state is completely recorded in the form of discrete symbols, providing standardized input for subsequent prediction; by making the historical semantic identifier sequence cover semantic identifiers within multiple historical time periods, the sequence carries the temporal evolution trajectory of user interests, providing a data foundation for capturing dynamic signals of interest migration; by organizing the sequence data at the granularity of the target user, prediction and recommendation can be carried out on a specific individual user, providing object-level input for personalized content distribution; by acquiring discrete identifier sequences with business semantics, the transformation from continuous user behavior to computable symbolic representation is realized.

[0045] Step 104: Input the historical semantic identifier sequence into the pre-trained prediction model and output the semantic identifier probability distribution in the future time period.

[0046] The predictive model is a sequence prediction framework that takes a user's historical semantic identifier sequence as input and outputs a probability distribution of future semantic identifiers. Specifically, the predictive model can include various model architectures capable of learning state transition patterns from discrete symbol sequences and outputting probabilistic prediction results. Its core function is to extract temporal dependency patterns from historical identifier sequences and, based on this, make probabilistic inferences about the next possible semantic identifier. The predictive model can complete parameter learning on large-scale user historical sequence data through offline pre-training, and only needs to perform forward computation during the inference phase to quickly output prediction results. The predictive model can exist in the form of a generalized interface, not bound to a specific model implementation. Any model that meets the interface specification of input being a discrete identifier sequence and outputting a probability distribution in the codebook space can be included in this framework. Furthermore, the probabilistic output format of the predictive model allows it to quantify the uncertainty of prediction, providing richer information for subsequent decisions than a single deterministic result. The pre-training paradigm enables the model to learn general interest state transition patterns from massive user historical sequence data, exhibiting good generalization prediction capabilities for both new users and cold start scenarios.

[0047] The future time period is the target prediction window for the predictive model, representing the time interval from the current moment where the user's semantic identifier state may change. Specifically, the future time period can include the next observation period immediately following the current moment, or it can include a further time window obtained through multi-step autoregressive prediction. Its length can be flexibly set according to the lead time requirements of business decisions. The future time period can maintain the same time granularity as the historical time period to ensure consistency between the predicted output and the historical input in terms of time resolution. Furthermore, the setting of the future time period reflects the leap of the recommendation system from passive response to proactive prediction. Its core significance lies in enabling the system to know the trend of change in the user's interest state before it actually changes, providing a time window for the forward-looking adjustment of the recommendation strategy. The specific length of the future time period needs to be reasonably configured by comprehensively considering the platform's content production cycle, user behavior frequency, and the characteristics of prediction accuracy decaying over time.

[0048] The semantic identifier probability distribution is a probability vector output by the prediction model in the codebook space, where each component corresponds to the predicted belonging probability value of a candidate semantic identifier. Specifically, the semantic identifier probability distribution can include the individual probability values ​​of each candidate semantic identifier in the codebook, and all probability values ​​satisfy a normalization constraint, i.e., the sum of the probabilities of all candidate identifiers equals 1. The semantic identifier probability distribution can be obtained by performing a normalization operation on the original score vector output by the model, and the magnitude of the probability value can reflect the model's confidence in the user's belonging to the corresponding interest cluster in the future time period.

[0049] Semantic identifier probability distributions can serve as an intermediate representation connecting model predictions and business decisions, transforming the complex problem of user behavior prediction into an intuitive probability ranking problem. Furthermore, the entropy value of the semantic identifier probability distribution reflects the degree of certainty in the prediction; a low-entropy distribution indicates a clear direction of user interest migration, while a high-entropy distribution indicates multiple possible migration directions. In practical applications, the semantic identifiers with the highest probability values ​​are typically extracted from the probability distribution as candidate prediction results to achieve a balance between coverage and computational complexity.

[0050] In practical applications, inputting historical semantic identifier sequences into a pre-trained prediction model and outputting the semantic identifier probability distribution within a future time period can be achieved through various methods.

[0051] One alternative approach is to convert each semantic identifier in the historical semantic identifier sequence into its corresponding discrete encoding index. After mapping this index to a continuous dense vector through an embedding layer, the vectors are concatenated in chronological order to form a sequence tensor. This tensor is then input into the encoding network of a pre-trained model for forward computation to obtain the hidden state representation. Finally, a classification output layer performs a normalization operation on the codebook space, outputting a probability distribution covering all candidate semantic identifiers. This method employs a standard sequence-to-distribution mapping process, offering advantages such as simplicity and fast inference speed, making it suitable for the high concurrency and low latency requirements of online real-time prediction scenarios.

[0052] Another alternative approach is to construct the historical semantic identifier sequence as an autoregressive input, using a pre-trained model to progressively process each identifier in the sequence. At each time step, the model's internal memory state is updated. After sequence processing, the original score vector for the next identifier is generated based on all accumulated contextual information. This vector is then temperature-scaled and normalized to obtain the semantic identifier probability distribution for future time periods. This method enhances the model's ability to model long-range dependencies by progressively accumulating historical information, resulting in better prediction accuracy in scenarios with long historical sequences and complex temporal patterns.

[0053] Another alternative approach is to employ a multi-step expansion strategy to predict the probability distribution of semantic identifiers over future time periods. First, the probability distribution for the most recent future time period is predicted. Then, the semantic identifier with the highest probability is selected from this distribution and appended to the end of the original sequence to form an expanded sequence. This expanded sequence is then re-inputted into the model for the next prediction step. This process is iteratively executed to cover a more distant future time window. This approach allows for flexible expansion of the prediction time span, making it suitable for business scenarios requiring medium- to long-term interest prediction and meeting the needs for predictions with differentiated time granularities.

[0054] In addition, an ensemble prediction strategy can be adopted, which uses multiple pre-trained sub-models with different structures or different subsets of training data to process the same historical semantic identifier sequence. The probability distributions output by each sub-model are weighted and averaged or fused by voting to obtain a more robust final probability distribution, which can effectively reduce the prediction bias that may exist in a single model.

[0055] In this step, by inputting historical semantic identifier sequences into a pre-trained prediction model, the temporal dependency patterns contained in the sequences are automatically extracted and utilized by the model, providing a computational basis for probabilistic prediction; by outputting the semantic identifier probability distribution in future time periods, the prediction results are presented in a probabilistic form, providing quantitative uncertainty information for subsequent decision-making.

[0056] Step 106: Determine the predicted semantic identifier based on the semantic identifier probability distribution, and determine the target user's interest migration direction based on the semantic association between the predicted semantic identifier and semantic identifiers in multiple historical time periods.

[0057] Predictive semantic identifiers are semantic identifiers determined from the probability distribution of semantic identifiers, representing the semantic identifiers most likely to belong to a target user within a future time period. Specifically, predictive semantic identifiers can include a single semantic identifier with the highest probability value in the probability distribution, or a prediction set consisting of the top several candidate identifiers selected after ranking by probability value. The specific selection method can be flexibly determined according to the different requirements of downstream decision-making regarding accuracy and coverage. Predictive semantic identifiers can serve as an intermediate bridge connecting the output of probabilistic models and deterministic business decisions, compressing the continuous probability space into actionable discrete decision signals. Furthermore, the process of determining predictive semantic identifiers is essentially a key step in interpreting the model prediction results for business applications. Since each semantic identifier corresponds to a user cluster with clear business meaning, predictive semantic identifiers naturally carry interpretable business semantic information. Operations personnel can directly understand the business meaning represented by the prediction results and formulate operational strategies based on this without additional technical translation.

[0058] Semantic association refers to the correspondence and degree of connection between predicted semantic identifiers and various semantic identifiers within a historical time period at the business semantic level. Specifically, semantic association can include similarity measurement, difference measurement, and evolutionary relationship determination between predicted and historical identifiers in the business semantic dimension, reflecting the continuity or change of user interest status in the time dimension. Semantic association can be determined by comparing the business attributes of the user clusters corresponding to predicted and historical identifiers, without relying on manually maintained static mapping tables.

[0059] Meanwhile, semantic associations can be verified using posterior data. That is, after obtaining the predicted semantic identifier, the actual behavioral data of the target user within a future time period is used to verify whether the business semantics corresponding to the predicted identifier meet expectations. Furthermore, because semantic identifiers have composite semantic characteristics, each identifier may simultaneously carry multiple dimensions of semantic information. When determining semantic associations, only the target semantic dimension related to the current business objective needs to be considered, without the need for a complete mapping of all semantic data. This posterior verification mechanism ensures the accuracy and reliability of semantic association judgments.

[0060] Interest migration direction refers to the trend and direction of a target user's interest state from the current historical time period to the future predicted time period. Specifically, interest migration direction can include two basic cases: interest cross-cluster jump represented by the difference between the predicted semantic identifier and the current semantic identifier, and interest state self-loop represented by the same predicted semantic identifier and the current semantic identifier.

[0061] Interest migration direction can serve as a core basis for adjusting recommendation strategies. Its clear directional information enables the recommendation system to develop targeted and differentiated content distribution plans. Furthermore, interest migration direction describes whether user interests have changed, and simultaneously depicts the specific direction of this change—that is, from which interest state to which interest state. Since each semantic identifier cluster has an understandable business meaning, interest migration direction naturally possesses business interpretability. Operations personnel can intuitively understand the reasons and direction for adjusting recommendation strategies, achieving effective integration between model prediction and business operations.

[0062] In practical applications, determining the predicted semantic identifier based on the semantic identifier probability distribution can be achieved through various methods.

[0063] One alternative approach is to select the semantic identifier with the highest probability value from the semantic identifier probability distribution as the predicted semantic identifier. This probability value is then compared with a pre-set confidence threshold. If the probability value is greater than or equal to the confidence threshold, the prediction is considered valid; otherwise, it is marked as a low-confidence state. This method is logically simple, has high decision-making efficiency, and provides a unique deterministic output, making it suitable for online recommendation scenarios with high real-time requirements.

[0064] Another alternative approach is to extract several candidate semantic identifiers with high probability rankings from the semantic identifier probability distribution to form a prediction candidate set. Then, a weighted summation is performed based on the probability values ​​of each candidate identifier in the prediction candidate set, and the semantic identifier corresponding to the weighted result is determined as the predicted semantic identifier. This method reduces the random bias that may be introduced by a single prediction result through multi-candidate comprehensive evaluation, and can still provide robust and reliable prediction results even when the probability distribution is relatively flat and the prediction uncertainty is high.

[0065] Another alternative approach is to progressively accumulate the probability values ​​of candidate semantic identifiers in the semantic identifier probability distribution, in descending order of probability. When the accumulated probability value first exceeds a preset coverage threshold, the accumulated set of candidate identifiers is determined as the prediction candidate set, and the identifier with the highest probability value is selected as the predicted semantic identifier. This method can adaptively determine the size of the candidate set according to the actual shape of the probability distribution, narrowing the candidate range to improve accuracy when the distribution is concentrated, and expanding the candidate range to ensure coverage when the distribution is dispersed.

[0066] In addition, all candidate identifiers whose probability values ​​exceed a preset probability threshold in the semantic identifier probability distribution can be directly identified as the predicted semantic identifier set. This set represents multiple interest clusters that a user may belong to in the future, providing a multi-path comparison basis for subsequent migration direction determination.

[0067] Once the predicted semantic identifier is determined, the direction of the target user's interest migration can be determined based on the semantic association between the predicted semantic identifier and semantic identifiers in multiple historical time periods.

[0068] One alternative approach is to compare the predicted semantic identifier with semantic identifiers from multiple historical time periods one by one to determine whether the predicted identifier and the most recent historical identifier belong to the same user cluster. Based on the comparison results, the direction of interest migration is determined to be either an inter-cluster jump or a self-loop within a cluster. This method is logically simple, has high decision-making efficiency, and provides a unique and deterministic output for the prediction result, making it suitable for online recommendation scenarios with high real-time requirements.

[0069] Another alternative approach is to map the predicted semantic identifiers and semantic identifiers from multiple historical time periods to the business semantic space. At the business semantic level, attribute difference analysis is performed between the predicted and historical identifiers. Based on the type and degree of attribute differences, the direction of the target user's interest migration and its corresponding business meaning are determined. This approach allows the determination of the migration direction to directly correspond to an understandable change in business meaning. Operations personnel can intuitively grasp the business essence of changes in user interests, facilitating the development of targeted operational intervention plans.

[0070] Another alternative approach is to determine the interest semantic features of the target user clusters corresponding to the predicted semantic identifiers based on posterior behavioral data. These interest semantic features are then compared with the interest semantic features of user clusters corresponding to the semantic identifiers across multiple historical time periods. The direction of interest migration is determined based on the comparison results. This approach establishes the determination of semantic associations on a business semantic basis validated by actual behavior, enhancing the credibility and interpretability of the migration direction determination.

[0071] In addition, a time decay mechanism can be introduced to determine semantic associations. Semantic identifiers in multiple historical time periods are assigned different weights according to their time proximity, so that recent historical identifiers have a higher weight in semantic association calculations, while distant historical identifiers provide a long-term preference background reference. The direction of interest migration of target users can be determined based on weighted semantic associations.

[0072] In this step, the predicted semantic identifier is determined based on the probability distribution of the semantic identifier, transforming the probabilistic model output into a discrete decision signal with clear business semantics, providing an operable comparison object for subsequent migration direction determination; by comparing the semantic association between the predicted semantic identifier and semantic identifiers in multiple historical time periods, the continuity or change of user interests in the time dimension is accurately characterized, providing a directional basis for adjusting the recommendation strategy; by determining the interest migration direction of the target user, the prediction result is elevated from the numerical level to the business semantic level, providing an interpretable decision-making basis for operators to understand the recommendation logic; through multi-dimensional comparison and post-validation of semantic associations, a reliable conversion from probabilistic prediction to business decision-making is achieved.

[0073] Step 108: Recommend target content that corresponds to the direction of interest migration to the target user.

[0074] Targeted recommended content refers to content items selected and pushed to target users that match the predicted direction of interest migration. Specifically, targeted recommended content can include various content items that are strongly correlated with the user clusters pointed to by the predicted direction of interest migration, covering a variety of content formats that can be distributed on the community platform, such as videos, live streams, text and images, and products. Targeted recommended content can be selected and determined from the resource pool of the corresponding content category based on the business semantics indicated by the direction of interest migration. Targeted recommended content can serve as the final content distribution result output by the recommendation system to the target user. Its selection logic is directly driven by the predicted future interest state rather than solely relying on the user's current behavioral preferences. Furthermore, the determination of targeted recommended content embodies the concept of predictive and forward-looking recommendation, which differs from the traditional recommendation system's passive response model based solely on users' historical behavior. The recommendation intervention logic has clear business semantics, allowing operators to clearly understand which type of content the system is currently strengthening or restricting in its recommendation distribution, and to adjust the content exposure structure in advance when it is necessary to guide user interests in a specific direction.

[0075] In practical applications, recommending targeted content that aligns with the direction of interest migration to target users can be achieved through various methods.

[0076] One alternative approach is to determine the target content category based on the direction of interest migration. Then, from the content resource pool corresponding to the target content category, content is sorted and filtered according to content quality scores, timeliness indicators, and diversity constraints. The top-ranked content items are identified as target recommended content and pushed to target users through the recommendation system's exposure channel. This method directly maps the migration direction to content category-level recommendation adjustments, resulting in a clear and highly controllable path. Operations personnel can clearly understand which types of content the system is strengthening or restricting in its recommendation distribution, facilitating strategy effect tracking and manual intervention.

[0077] Another alternative approach is to introduce interest migration direction as an additional ranking signal into the candidate set scoring stage of the existing recommendation ranking model. Candidate content items that match the content category indicated by the migration direction are given a positive ranking weight, while those that do not are penalized negatively. The final target recommendation content pushed to the target user is determined based on the adjusted comprehensive ranking score. This approach integrates migration direction guidance information without changing the overall architecture of the existing recommendation system, achieving incremental upgrades to forward-looking recommendation capabilities with minimal system modification costs.

[0078] Another option is to build a personalized recommendation content candidate pool for target users based on their interest migration direction. The content in the candidate pool is prioritized according to its match with the migration direction. Each time a target user makes a content request, the recommended content is first selected from the personalized candidate pool. If the candidate pool is insufficient, it is supplemented from the general content pool using a standard strategy. This approach ensures that the target recommended content highly matches the predicted interest change direction, quickly responding and pushing matching content when user interests shift, thus improving the alignment between recommended content and user expectations.

[0079] In addition, the direction of interest migration can be encoded as contextual feature information in the recommendation request, and input into the recommendation ranking model together with user profile features, content attribute features, and scene features. The model automatically learns the influence weight of the migration direction on content preference during feature interaction, and outputs the target recommended content ranking result adapted to the direction of interest migration, thus avoiding the problem of reduced recommendation diversity that may be caused by rigid rules.

[0080] In this step, by recommending target content that corresponds to the direction of interest migration to the target user, the abstract interest prediction result is transformed into a specific content recommendation action, providing an execution path from user interest prediction to precise content delivery; by making recommendation decisions based on the predicted future interest state rather than relying solely on current behavior, the recommendation system has a first-mover advantage in the time dimension, providing a decision basis for forward-looking content distribution.

[0081] In this embodiment, a historical semantic identifier sequence is constructed by acquiring semantic identifiers of the target user across multiple historical time periods. This discrete semantic identifier serves as the basic unit for interest modeling, decoupling the interest prediction task from the main recommendation task and providing independent optimization objectives and evaluation dimensions. By inputting the historical semantic identifier sequence into a pre-trained prediction model, the model outputs a probability distribution of semantic identifiers for future time periods, quantifying the uncertainty of the user's future interest state in a probabilistic manner and providing a confidence basis for subsequent decisions. Predicted semantic identifiers are determined based on the semantic identifier probability distribution, and the direction of interest migration is determined based on the semantic association between the predicted semantic identifiers and the semantic identifiers within the historical time periods. This transforms the prediction results at the discrete encoding level into a description of interest changes with business semantics, making the recommendation intervention logic interpretable. By recommending target content corresponding to the direction of interest migration to the target user, the system provides predicted content based on the predicted future interest state rather than the current state, expanding the effective time window of the recommendation strategy. This results in more accurate prediction of user interest migration, improving the quality of recommended content and recommendation effectiveness.

[0082] In an optional embodiment of this specification, obtaining the historical semantic identifier sequence of the target user includes: According to the preset snapshot interval, the semantic identifiers of the target user are periodically sampled to obtain semantic identifier snapshots corresponding to each time period in multiple historical time periods. Arrange the semantic identifier snapshots corresponding to each time period in chronological order to obtain the historical semantic identifier sequence.

[0083] The preset snapshot interval is a fixed time window parameter used to define the semantic identifier sampling period. It determines the frequency at which the system periodically records the semantic identifier status of the target user. Specifically, the preset snapshot interval can include various time granularity schemes such as a fixed period configuration based on natural days, a flexible period configuration based on the platform's business operation rhythm, and an adaptive period configuration based on user behavior density statistical characteristics.

[0084] Furthermore, the preset snapshot interval can serve as the smallest prediction unit for the prediction model, meaning the model uses each snapshot interval as the basic time step for sequence extrapolation and future state prediction. Setting the preset snapshot interval requires a comprehensive consideration of the balance between platform business rhythm, user behavior frequency, and prediction accuracy requirements. An interval that is too short will introduce excessive behavioral noise, leading to sequence redundancy and wasted computational resources, while an interval that is too long may miss key interest migration signals, resulting in insufficient prediction resolution.

[0085] A semantic identifier snapshot is an instantaneous sampling record of a target user's semantic identifier status within a specific time window. It fully records the semantic identifier code and associated metadata to which the user belongs during that time period. Specifically, a semantic identifier snapshot may include structured fields such as snapshot generation timestamp, user unique identifier, corresponding semantic identifier code, snapshot sequence number, and snapshot validity period.

[0086] Furthermore, semantic tag snapshots can serve as the basic data units for constructing historical semantic tag sequences. Each snapshot corresponds to a specific time position in the sequence, and multiple snapshots are sequentially stitched together to form a complete trajectory of user interest evolution. The generation of semantic tag snapshots is the primary execution step in the orientation strategy scheduling mechanism. The system automatically triggers a sampling operation when each snapshot period arrives, ensuring the continuity and integrity of the sequence data in the time dimension, and providing a real-time updated input data source for the online inference of the prediction model.

[0087] In practical applications, the semantic identifiers of target users are periodically sampled according to a preset snapshot interval to obtain semantic identifier snapshots corresponding to each time period. This can be achieved through various methods.

[0088] One alternative approach is to automatically initiate a sampling task based on a fixed-time-window trigger mechanism when the system clock reaches an integer multiple of a preset snapshot interval. The sampling service reads the semantic identifier code calculated by the residual quantization model for the target user's current moment, and encapsulates this code, along with the sampling timestamp, user unique identifier, and snapshot sequence number, into a structured snapshot record, which is then persistently stored in a distributed time-series database. Based on the periodic execution results of the sampling task, the system can continuously accumulate the semantic identifier state data of the target user at a constant time granularity, forming a time-series sample set with equal intervals. Through a rigid scheduling strategy with fixed intervals, it can ensure that the snapshot sequences of different users are strictly aligned on the timeline, providing a regular and consistent input data foundation for subsequent cross-user batch sequence modeling.

[0089] Another alternative approach is to use a business event-driven scheduling strategy to bind snapshot sampling tasks to the platform's existing scheduled batch processing jobs. This involves embedding the snapshot generation process within the daily offline data pipeline execution, leveraging the parallel computing capabilities of the distributed batch processing framework to simultaneously perform semantic identifier sampling operations on all active users, and writing the sampling results to the corresponding snapshot storage table according to user identifier partitions. By reusing existing data processing infrastructure and scheduling resources, periodic snapshot collection for tens of millions of users can be completed without increasing additional system load, significantly reducing the marginal computational cost of snapshot generation.

[0090] Another alternative approach is to dynamically adjust the sampling triggering conditions based on the statistical characteristics of user behavior density. During periods of high user activity, the standard snapshot interval is maintained to ensure the integrity of information capture; during periods of low user activity, the sampling period is appropriately extended to reduce the generation of invalid snapshots. Based on an adaptive scheduling strategy, snapshot data with balanced information density is generated for users with different activity levels. This elastic sampling mechanism can maximize the effective information content of the snapshot sequence while keeping overall storage overhead under control, avoiding the generation of a large number of repetitive and redundant records for inactive users.

[0091] In addition, based on a dual mechanism combining incremental sampling and full verification, incremental sampling can be performed only on users whose behavior has changed during the regular snapshot period to reduce the real-time computing pressure. At the same time, a complete semantic identifier sampling is performed on all users according to a longer verification period. The accuracy and consistency of the snapshot sequence are ensured by cross-comparison and verification of incremental data and full data, and compensation sampling operations can be automatically triggered when data deviations are detected.

[0092] Once a semantic identifier snapshot is obtained, the semantic identifier snapshots corresponding to each time period can be arranged in chronological order to obtain a historical semantic identifier sequence.

[0093] One alternative approach is to perform an ascending sort on all snapshot records accumulated by the target user across multiple historical time periods, based on the sampling timestamp field carried in the snapshot records. The semantic identifier codes corresponding to each position in the sorted snapshot sequence are then extracted and concatenated into an ordered encoded sequence. Meta-information fields such as user identifier and sequence length are appended to the beginning of the sequence. Based on the sorting and concatenation results, the system can generate a semantic identifier trajectory record that strictly follows chronological order, providing standardized and semantically complete input data for the prediction model.

[0094] Another alternative approach is to index and arrange snapshots based on logical time step numbers. The first snapshot period is marked as the time step, and subsequent periods are numbered sequentially, constructing an ordered mapping table with logical step numbers as keys and semantic identifier codes as values. The complete historical semantic identifier sequence is obtained by traversing the key order of the mapping table. Through this logical numbering mechanism, even if sampling is missing in individual periods due to system failures or user offline status, the sequence can still maintain structural continuity and parsability. Missing positions can be filled with default placeholders without affecting the overall structural integrity of the sequence.

[0095] In addition, online sorting and sequence splicing operations can be performed on real-time snapshot data based on the streaming processing framework. By maintaining a fixed-length sliding window buffer for each user, snapshot data is immediately appended to the corresponding time position when it arrives. When the amount of effective data in the window meets the preset sequence length condition, the sequence output is triggered, realizing low-latency online construction and real-time supply of historical semantic identifier sequences.

[0096] In the embodiments of this specification, by periodically sampling the semantic identifiers of target users according to a preset snapshot interval, the changes in users' interest states over continuous time can be transformed into discrete, equally spaced observation records, providing standardized input data with a regular structure and balanced information density for the prediction model. By arranging the semantic identifier snapshots of each time period in chronological order to construct a historical semantic identifier sequence, the temporal dependencies and state transition trajectories of user interest evolution can be completely preserved, enabling the prediction model to fully capture the dynamic evolution patterns and long-term trend characteristics of user behavior, providing a data foundation for subsequent prediction of semantic identifiers.

[0097] In one optional embodiment of this specification, a historical semantic identifier sequence is input into a pre-trained prediction model, which outputs a semantic identifier probability distribution over a future time period, including: Based on the transition relationships between semantic identifiers in the historical semantic identifier sequence, a prediction model is used to model the sequence and output the probability value of each candidate semantic identifier in the future time period. Based on the probability values ​​of each candidate semantic identifier, the probability distribution of the semantic identifier is determined.

[0098] Jump relationships are a temporal feature concept describing changes in the semantic identifier encoding of a target user between adjacent snapshots, reflecting the migration direction of user interests or the evolutionary trend of behavioral patterns. Specifically, jump relationships can include two basic types: self-loop jumps and non-self-loop jumps. Self-loop jumps indicate that the user's semantic identifier remains unchanged within adjacent snapshot periods, reflecting the stability and persistence of the user's interest state; non-self-loop jumps indicate that the user's semantic identifier encoding has switched, reflecting the migration and leap of user interests from one semantic cluster to another.

[0099] Jump relationships can serve as core input features for predictive models in sequence modeling. By learning jump patterns in historical sequences, models infer future state transition directions and cluster changes. Jump relationships not only contain information about state changes at adjacent times but also implicitly contain multi-dimensional temporal statistical patterns such as jump frequency, jump amplitude, and jump direction preferences. These patterns collectively constitute the basic feature space for depicting the dynamic profile of user interest evolution.

[0100] Sequence modeling is a modeling process that learns temporal dependencies and predicts future states in semantic identifier transition sequences. Its goal is to extract state transition patterns from historical identifier sequences and output the probability distribution of each state at the next time step. Specifically, sequence modeling can include several technical steps, such as parameterizing the transition probabilities between identifiers at different positions in the sequence, effectively capturing long-range temporal dependencies spanning multiple time steps, and jointly modeling periodic oscillation patterns and trend drift changes in the sequence. Furthermore, sequence modeling can employ a general prediction framework that takes the semantic identifier sequence as input and the probability distribution of the semantic identifiers at the next time step as output. This framework does not impose specific limitations on the network structure implementation at the methodological level and has broad model compatibility. The formal expression of sequence modeling can be a conditional probability distribution P(next semantic identifier | all historical semantic identifiers), which, given all historical semantic identifier observations from the first time step to the current time step, infers the complete probability distribution of the semantic identifier value at the next time step.

[0101] Candidate semantic identifiers are the set of identifier codes in the codebook space that can be predicted by the model as semantic identifiers for the next time step. They cover all semantic cluster categories defined by the residual quantization model. Specifically, candidate semantic identifiers can include cluster identifiers that are highly related to the target user's historical interest domain, cluster identifiers that correspond to the current trend of popular content on the platform, and other cluster identifiers in the codebook space. The model needs to provide a clear and comparable probability assessment for each candidate identifier in the set.

[0102] Candidate semantic identifiers can be used as enumeration objects for the output layer of the prediction model. The dimension of the model output vector strictly corresponds to the total number of candidate identifiers in the codebook space, ensuring the completeness of prediction coverage. Furthermore, the size of the candidate semantic identifier set is determined by the codebook capacity of the residual quantization model. A larger codebook space means finer granularity of user clustering and richer prediction resolution, but it also places higher demands on the computational efficiency of the model output layer.

[0103] The probability value is the normalized prediction score output by the prediction model for each candidate semantic identifier, representing the likelihood that the candidate identifier will be identified as the target user's identifier at the next moment. Specifically, the probability value can include various numerical expressions such as the standardized probability obtained by exponentially transforming and globally summing the original output score of the model through the Softmax normalization function, the smoothed probability adjusted by the temperature coefficient, and the conditional probability after truncation sampling.

[0104] Probability values ​​can serve as a quantitative decision-making basis for determining the final predicted semantic identifier from the prediction results. The candidate identifier with the highest probability is the most likely future cluster to which the target user belongs, while the identifier with the second highest probability constitutes the alternative prediction results. Furthermore, the sum of the probability values ​​of all candidate semantic identifiers equals 1. This normalization constraint ensures the mathematical completeness and closure of the probability distribution, making the probability values ​​between different candidate identifiers directly comparable and providing a reliable numerical basis for the ranking and selection of downstream recommendation strategies.

[0105] In practical applications, based on the transition relationships between semantic identifiers in historical semantic identifier sequences, sequence modeling is performed through a prediction model to output the probability values ​​of each candidate semantic identifier in future time periods. This can be achieved through various methods.

[0106] One alternative approach is to encode the transition relationships between adjacent time-series identifiers in the historical semantic identifier sequence into a multi-dimensional transition feature vector. The transition features across all time steps are concatenated chronologically and input into the sequence encoding layer of the prediction model. This encoding layer abstracts and semantically compresses the temporal dependencies layer by layer. Finally, a classification output layer covering all candidate semantic identifiers is connected to the output of the encoding layer. The original prediction score for each candidate identifier is then calculated via this classification output layer. Based on the hierarchical representation capability of the sequence encoding layer for transition patterns, the model can simultaneously capture short-term local transition patterns and long-term global evolution trends, outputting a candidate identifier probability assessment that combines timeliness and a global perspective.

[0107] Another alternative approach is to perform stepwise conditional modeling of historical semantic identifier sequences based on an autoregressive generative paradigm. At each time step, the model uses all historical identifier observations up to the current moment as the conditional context to calculate the conditional probability of each candidate identifier for the next moment. The joint probability distribution is then transformed into an ordered product of a series of conditional probabilities through chain decomposition. This autoregressive modeling approach allows the model to naturally handle variable-length input sequences and fully utilize all observed historical information at each prediction step, gradually narrowing prediction uncertainty.

[0108] Another alternative approach is to construct an empirical transition matrix based on the frequency and direction of transitions between identifiers in the historical semantic identifier sequence. This transition matrix can then be injected as structured prior knowledge into the attention weight calculation process of the prediction model. This allows the model to learn data-driven features while incorporating Markov transition prior information, generating probability values ​​for each candidate identifier at the model output that fuse statistical priors and learned features. This collaborative fusion mechanism of statistical priors and deep learning can effectively improve the model's robustness and generalization ability in predicting low-frequency transition patterns even with a limited number of training samples during cold starts.

[0109] Furthermore, historical semantic identifier sequences can be sliced ​​hierarchically based on multi-scale time windows. High-frequency local jump features and instantaneous interest fluctuations can be captured in short time windows, while low-frequency global trend features and steady-state interest preferences can be captured in long time windows. The multi-scale features are adaptively aggregated through a weighted fusion layer and then input into a unified prediction output layer to output the probability values ​​of each candidate semantic identifier in the future time period, thereby realizing the collaborative modeling and complementary enhancement of multi-granular temporal information.

[0110] By outputting the probability values ​​of each candidate semantic identifier, the probability distribution of the semantic identifier can be determined based on the probability values ​​of each candidate semantic identifier.

[0111] One alternative approach is to input the original score vector calculated by the output layer of the prediction model for all candidate semantic identifiers into the Softmax normalization function. This function maps the original scores to the positive real number domain through element-wise exponentiation, and then performs normalized division with the sum of the exponents of all candidate identifiers as the common denominator. This yields a standard probability value for each candidate identifier between zero and one, with the total probability strictly equal to one. Arranging all normalized probability values ​​in the encoding order of the candidate identifiers constitutes the complete semantic identifier probability distribution vector. Due to the monotonically order-preserving property of the Softmax function, candidate identifiers with higher original scores receive higher probability values ​​after normalization, and the partial order relationship predicted by the model remains unchanged during the normalization process.

[0112] Another alternative approach is to apply a temperature coefficient to the model's original output scores before Softmax normalization. A temperature coefficient greater than one can smooth the probability distribution, enhancing the exploratory and diverse nature of the predictions; conversely, a temperature coefficient less than one can make the probability distribution more sharply concentrated, enhancing the certainty and discriminative power of the predictions. The appropriate temperature parameter is selected based on the downstream recommendation strategy's preference for exploration and utilization, and the final semantic label probability distribution is determined based on the adjusted probability values. This temperature adjustment mechanism allows for flexible control of the concentration and entropy levels of the prediction results without retraining the model parameters.

[0113] Another alternative approach is to impose prior constraints on the candidate semantic identifier set based on the business scenario. Candidate identifiers that do not conform to the current recommendation strategy or content security rules have their probability values ​​set to zero or are subject to an exponential penalty factor. The probability values ​​of the remaining valid candidate identifiers are then re-normalized. This process determines the corrected semantic identifier probability distribution while satisfying the business constraints. This probability correction mechanism based on prior constraints seamlessly integrates platform business rules and operational strategies into the model prediction process, preventing conflicts between recommendation results and the platform's content ecosystem.

[0114] Furthermore, multiple rounds of independent prediction can be performed on the same input sequence based on multiple forward inferences. The arithmetic mean or median of the probability values ​​of each candidate identifier obtained from multiple rounds of predictions can be used as the final probability estimate. By integrating a smoothing mechanism, the random fluctuations and variance of a single inference can be reduced, and a more reliable semantic identifier probability distribution can be determined based on the robust probability values ​​after integration.

[0115] In the embodiments of this specification, by performing sequence modeling based on the jump relationships between semantic identifiers in the historical semantic identifier sequence, the discrete migration trajectory of user interest state can be transformed into a computable temporally dependent feature representation. This allows the prediction model to fully learn the evolutionary laws of user behavior patterns and the inherent logical structure of state transitions. At the same time, it extends deterministic single-point prediction to probabilistic prediction covering all candidate identifiers, providing rich uncertainty quantification information and a flexible multi-path decision space for downstream recommendation strategies.

[0116] In one optional embodiment of this specification, the pre-trained prediction model includes at least one of a lightweight baseline model, a recurrent neural network model, a self-attention regression model, and a graph neural network model. Based on the transition relationships between semantic identifiers in historical semantic identifier sequences, a prediction model is used to model the sequence and output the probability values ​​of each candidate semantic identifier in future time periods, including: When the prediction model is a lightweight baseline model, the state transition probability is determined based on the frequency of jumps between adjacent semantic identifiers in the historical semantic identifier sequence, and the probability value of each candidate semantic identifier in the future time period is output based on the state transition probability. When the prediction model is a recurrent neural network model, each semantic identifier in the historical semantic identifier sequence is mapped to a continuous vector representation. The continuous vector representation is sequence encoded by the recurrent neural network model to obtain the hidden state representation. Based on the hidden state representation, the probability value of each candidate semantic identifier in the future time period is output. When the prediction model is a self-attention regression model, each semantic identifier in the historical semantic identifier sequence is mapped to a continuous vector representation. The continuous vector representation is then self-attention encoded by the self-attention autoregression model to obtain the sequence context representation. Based on the sequence context representation, the probability value of each candidate semantic identifier in the future time period is output. When the prediction model is a graph neural network model, a directed graph is constructed based on the historical jump relationship between user clusters corresponding to each semantic identifier. The directed graph is modeled by the graph neural network model to output the probability value of each candidate semantic identifier in the future time period.

[0117] Lightweight baseline models are lightweight probabilistic models for sequence prediction based on state transition probability statistics. Their core principle is to directly estimate the conditional probability distribution using the frequency of state transitions in historical observation data, without requiring a parameterized training process. Specifically, lightweight baseline models can include various order configurations such as first-order Markov chains, second-order Markov chains, and even higher-order N-order Markov chains. The N-order Markov chain models the conditional probability distribution of the semantic identifier at the next time step, using the previous N semantic identifier states as conditions.

[0118] Furthermore, lightweight baseline models can serve as rapid validation baselines within a predictive model framework, enabling probabilistic predictions of semantically labeled sequences without requiring GPU training resources or large-scale parameter optimization. Lightweight baseline models offer significant advantages in computational efficiency and interpretability, as their prediction results are directly derived from observable historical jump statistics. Operators can clearly trace the statistical basis of each probability value, providing unique transparency value in model debugging and result auditing.

[0119] Recurrent Neural Network (RNN) models are deep learning models that progressively encode sequential data using a recurrent connection structure. Internally, they maintain a hidden state vector that is recursively updated over time steps, enabling them to capture temporal dependencies across multiple time steps in a sequence. Specifically, RNN models can include various variant structures such as basic recurrent neural networks, long short-term memory networks, and gated recurrent units, each with its own advantages in memory retention and gradient propagation characteristics.

[0120] Furthermore, recurrent neural network (RNN) models can map discrete semantic identifiers into dense representations in a continuous vector space through learnable embedding layers. Based on a recurrent structure, they progressively encode continuous vector sequences, outputting the probability distribution of each candidate semantic identifier at the next time step after sequence processing. The parameterized modeling approach of RNN models enables them to automatically learn complex nonlinear transition patterns from large-scale training data, exhibiting stronger expressive power and generalized prediction capabilities for unseen transition combinations compared to lightweight baseline models.

[0121] Self-attention regression models are deep learning models that directly model the dependencies between any two positions in a sequence based on the self-attention mechanism. They achieve efficient capture of long-range dependencies by computing the attention weights between all position pairs in parallel. Specifically, self-attention regression models can include various deep configurations such as single-layer self-attention encoders, multi-layer stacked self-attention encoders, and complete autoregressive transformer architectures that integrate feedforward networks and layer normalization.

[0122] Furthermore, the self-attention regression model can use each semantic identifier in the historical semantic identifier sequence as input to the word sequence. Through a causal masking mechanism, it ensures that each position can only focus on information from its previous positions, achieving parallel perception of the entire historical context while maintaining the autoregressive generation characteristics. The self-attention regression model has significant advantages in modeling scenarios with long historical windows and complex jump patterns. Its attention weight distribution can intuitively reflect the degree of contribution of each historical identifier in the sequence to the current prediction, providing a traceable attention attribution explanation for the prediction results.

[0123] Graph neural network (GNN) models are deep learning models that model the relationships between nodes in graph-structured data using a message-passing mechanism. Their core operation involves each node updating its own representation vector by aggregating feature information from its neighbors. Specifically, GNN models can include various architectural variants such as graph convolutional networks, graph attention networks, and message-passing neural networks, each differing in its neighbor aggregation method and attention allocation strategy.

[0124] Furthermore, graph neural network models can model the transition relationships between semantic identifiers as edge structures in a directed graph, and the user clusters corresponding to each semantic identifier as nodes in the graph. Through multiple rounds of message passing, each node integrates the transition pattern information of its reachable neighbors, and predicts the most likely successor node based on the fused node representation. Graph neural network models are naturally suitable for processing non-Euclidean data with rich relational structures. In scenarios where the transition relationships between semantic identifiers present complex topological structures, they can capture high-order transition paths and global graph structure features that are difficult for traditional sequence models to perceive.

[0125] Jump frequency is the cumulative number of state transitions between two semantic identifiers, statistically analyzed from historical observation data. It quantifies the frequency with which a user group migrates from one semantic cluster to another. Specifically, jump frequency can include various statistical methods such as the total number of non-self-loop jumps between each pair of semantic identifiers in the full user historical data, the number of periodic jumps segmented by time window, and the number of conditional jumps stratified by user group. Jump frequency can serve as the primary statistical basis for estimating state transition probabilities in lightweight baseline models. The corresponding conditional transition probabilities can be obtained by normalizing the jump frequencies of each successor state under a specific predecessor state. Furthermore, the statistical granularity of jump frequency directly affects the reliability of probability estimation. Probability estimates corresponding to high-frequency jumps have lower statistical variance and higher confidence, while probability estimates corresponding to low-frequency jumps require correction using smoothing techniques or prior distributions to avoid overfitting.

[0126] State transition probability is a conditional probability value describing the likelihood of a user transitioning from the current semantic identifier state to a specific subsequent semantic identifier state. Mathematically, it is the conditional probability of the next state taking a specific value given the current state. Specifically, state transition probability can include various estimation forms, such as transition probabilities depending only on the current state under first-order conditions, joint conditional probabilities depending on combinations of the previous N states under higher-order conditions, and smoothed transition probabilities corrected by Laplace smoothing or Goodturing estimation. State transition probability can serve as a direct numerical carrier for the prediction results output by a lightweight baseline model. The transition probabilities corresponding to each subsequent state, after normalization, constitute the probability distribution of each candidate semantic identifier within a future time period. Furthermore, the estimation quality of state transition probability depends on the sufficiency of historical observation data and the degree to which the assumption of stationarity of user behavior patterns holds. Under conditions where the user group is large enough and the behavior patterns are relatively stable, frequency estimation can converge to the true transition probability distribution.

[0127] Continuous vector representation (CV) is a dense vector obtained by mapping discrete semantic identifiers to a continuous real-number space. It transforms originally incomputable symbolic identifiers into numerical feature representations that support gradient optimization and similarity operations. Specifically, CV can be generated in various ways, including fixed-dimensional dense vectors obtained through lookup tables of learnable embedding matrices, context-dependent vectors obtained through encoding by pre-trained language models, and multi-layer composite vectors obtained by concatenating residual quantized codebook vectors. CV can serve as input feature forms for recurrent neural network models and self-attention regression models, giving discrete identifiers that originally only had equivalence or ordinal relationships rich geometric spatial structures, enabling models to learn semantic similarity between identifiers through distance and direction relationships between vectors. Furthermore, the dimensionality selection and initialization strategy of CV significantly impact the model's convergence speed and final prediction performance. Too low a dimensionality can lead to information bottlenecks and insufficient discriminative power in the vector representations of different semantic identifiers, while too high a dimensionality increases the number of parameters and the risk of overfitting.

[0128] The hidden state representation is an internal memory vector maintained by a recurrent neural network (RNN) model during sequence data processing. It encodes a compressed summary of all observed information from the start of the sequence to the current processing position. Specifically, the hidden state representation can include various forms of internal states, such as a single hidden layer vector in a basic RNN, separate cell state vectors and hidden state vectors in a long short-term memory (LSM) network, and candidate hidden vectors in a gated recurrent unit after being controlled by update and reset gates. The hidden state representation serves as an information carrier for the sequence encoding results transmitted from the RNN model to the output layer. After sequence processing, the model calculates the prediction scores for each candidate semantic identifier based on the hidden state representation at the final time step. Furthermore, the information capacity of the hidden state representation is limited by its fixed dimension, which may lead to early information decay and gradient vanishing problems when processing longer sequences. LSM networks and gated recurrent units effectively alleviate the difficulty of preserving long-range information through gating mechanisms.

[0129] Self-attention encoding is the core computational process in self-attention regression models that models the global dependencies of the input sequence. It calculates the attention weights of each position in the sequence to all other positions and performs a weighted sum to obtain a positional representation that incorporates the global context. Specifically, self-attention encoding can include various configurations such as single-head self-attention computation, multi-head parallel self-attention computation, and unidirectional self-attention computation combined with causal masks. The causal mask ensures that each position can only access information from its previous positions during the autoregression generation process. Self-attention encoding can transform the continuous vector representations of each position in the historical semantic identifier sequence into a context-enhanced representation that incorporates the global dependency structure of the sequence, enabling the output vector of each position to simultaneously encode local neighborhood information and the global sequence pattern. Furthermore, the computational complexity of self-attention encoding is proportional to the square of the sequence length. When the historical window is long, approximation algorithms such as sparse attention or linear attention can be used to reduce computational overhead, improving inference efficiency while maintaining modeling accuracy.

[0130] Sequence context representation is the sequence representation output by a self-attention regression model after it has performed self-attention encoding on a sequence of historical semantic identifiers. It integrates all historical information and comprehensively reflects the dependencies and global evolution patterns among the identifiers in the sequence. Specifically, sequence context representation can include various extraction methods such as the output vector of the last position in the sequence, the weighted pooling result of the output vectors at all positions, and the global representation aggregated after special classification label positions. Sequence context representation can serve as a direct input feature for the predicted probability distribution output by the self-attention regression model. The model calculates the predicted score of each candidate semantic identifier based on the global dependency information encoded in the sequence context representation and outputs the probability distribution after normalization. Furthermore, compared to the hidden state representation of recurrent neural networks, sequence context representation has the advantage of preserving more complete information. Because the self-attention mechanism directly accesses all historical positions in parallel, there is no early information loss problem caused by gradual information compression, providing richer historical context support in long sequence modeling tasks.

[0131] A directed graph is a mathematical structure consisting of a set of nodes connected by directional edges. Each edge points from a source node to a target node, expressing a directional relationship between the two nodes. Specifically, directed graphs can include various construction forms such as transition graphs with semantic identifiers as nodes and the direction of transitions between identifiers as edge directions, weighted directed graphs that encode historical transition probabilities with edge weights, and complete directed graphs that include self-loop edges to represent state preservation. Directed graphs can serve as input data structures for relational modeling in graph neural networks. The topological connections between nodes and the distribution of edge weights in the graph jointly encode global transition pattern information between semantic identifiers. Furthermore, the construction quality of the directed graph directly affects the modeling performance of the graph neural network model. The directionality of the edges ensures that the model can distinguish the asymmetry between forward and backward transitions, and the probabilistic encoding of edge weights enables the model to aggregate neighbor information differently according to the probability of transition during message passing.

[0132] User clusters are groups of users with similar behavioral characteristics and interests, partitioned in the user representation space by a residual quantization model. Each semantic identifier uniquely corresponds to a user cluster with clear business semantics. Specifically, user clusters can include various business type classifications such as high-consumption-value user clusters, high-activity user clusters, user clusters with specific category preferences, and user clusters with emerging interests. Different clusters exhibit significant differences in user value contribution and behavioral patterns. User clusters can serve as business entity carriers of nodes in a directed graph. Each node represents a user group with interpretable business meaning, and the directed edges between nodes reflect the interest migration paths and conversion relationships between user groups. Furthermore, the granularity of user cluster partitioning is determined by the codebook capacity of the residual quantization model. Finer-grained cluster partitioning provides more accurate user group descriptions but also increases the complexity of the number of nodes and edge relationships in the directed graph, placing higher demands on the modeling capacity of the graph neural network model.

[0133] Historical jump relationships are the frequency distribution and probability relationships of state transitions between semantic tags, statistically derived from the full set of user historical behavior data. They depict the macro-statistical patterns of user groups migrating between different interest clusters. Specifically, historical jump relationships can include various relational dimensions such as bidirectional jump frequency statistics between any two semantic tags, conditional transition probability distribution from a specific predecessor tag to all successor tags, and statistics of multi-order transition paths spanning multiple time steps. Historical jump relationships can serve as the data foundation for constructing directed graphs. By extracting the jump frequency between tag pairs from the full set of historical data and converting it into weight values ​​for directed edges, a weighted directed graph reflecting the global topology of user group interest migration can be constructed. Furthermore, historical jump relationships have a non-stationary characteristic of slowly evolving over time. When constructing directed graphs, time decay weighting or sliding window statistical strategies can be adopted to ensure that edge weights more accurately reflect recent transfer trends rather than being overly diluted by long-term historical data.

[0134] In practical applications, based on the transition relationships between semantic identifiers in the historical semantic identifier sequence, a prediction model is used to model the sequence and output the probability value of each candidate semantic identifier in the future time period. The corresponding implementation method can be adopted according to the specific type of the prediction model.

[0135] One alternative approach is to iterate and statistically analyze the transition frequencies between adjacent time-series semantic identifiers in the entire user's historical semantic identifier sequence, using a lightweight baseline model as an example. For instance, with a second-order Markov chain, the frequencies of all triples (i.e., the previous time-series identifier, the current time-series identifier, and the next time-series identifier) ​​can be accumulated. Based on a specific predecessor identifier combination, the transition frequency of each successor identifier is divided by the total occurrence count of that predecessor combination to calculate the conditional transition probability, specifically represented as: P(SID_{t+1} | SID_t, SID_{t-1}, …, SID_{t-N+1}). The conditional transition probabilities corresponding to all candidate semantic identifiers can be arranged in encoding order to output the probability values ​​of each candidate semantic identifier within the future time period. The transition probabilities obtained through frequency estimation require no parameter training process and, given sufficient historical data, can quickly generate statistically reliable prediction results. Furthermore, each probability value can be traced back to specific historical transition events, providing complete transparency and interpretability.

[0136] Another alternative approach, when the prediction model is a recurrent neural network (RNN), is to encode each discrete identifier in the historical semantic identifier sequence into a fixed-dimensional continuous vector representation using a learnable embedding matrix. This continuous vector representation is then sequentially input into the RNN model. At each time step, the model calculates the hidden state representation based on the current input vector and the hidden state of the previous time step. After processing all identifiers in the sequence, the final hidden state representation is obtained. Specifically, this can be represented as: h_t = RNN(e_{SID_t}, h_{t-1}), P(SID_{t+1}) = Softmax(W h_t + b). A linear transformation layer and the Softmax normalization function are used to calculate and output the probability values ​​of each candidate semantic identifier in the codebook space. Through the progressive information accumulation mechanism of the recurrent structure, the model can compress and encode all temporal dependencies of the historical sequence in the hidden state, still possessing generalization prediction capabilities for identifier combinations not directly observed in the training data.

[0137] Another alternative approach is to map each identifier in the historical semantic identifier sequence into a continuous vector representation and append positional encoding information through an embedding layer, when the prediction model is a self-attention regression model. The resulting vector sequence with added positional encoding is then input into the self-attention regression model. The model performs parallel computation on the dependencies between any two positions in the sequence through multi-layer causal self-attention encoding. Under causal masking constraints, each position only aggregates information from its preceding positions to maintain autoregressive generation characteristics. Based on the sequence context representation output from the last position of the sequence, the probability values ​​of each candidate semantic identifier are output after linear projection and Softmax normalization, specifically expressed as: P(SID_{t+1} |SID_{1:t}) = Transformer_θ(e_{SID_1}, …, e_{SID_t}). Through the global parallel modeling capability of the self-attention mechanism, the model can simultaneously perceive the dependencies between all identifiers in the historical sequence in a single forward computation, exhibiting superior modeling accuracy and training efficiency compared to recurrent structures in scenarios with long historical windows and complex transition patterns.

[0138] Another alternative approach, when the prediction model is a graph neural network, involves constructing a weighted directed graph based on the historical transition relationships between clusters corresponding to each semantic identifier in the full user history data. Using each semantic identifier cluster as a node and the transition probability between identifiers as the weight of the directed edges, the semantic identifier currently belonging to the target user is determined as the source node. Through multiple rounds of neighbor message passing and feature aggregation operations performed by the graph neural network model, the source node gradually integrates the transition pattern information of its reachable successor nodes and global graph structure features. Based on the aggregated source node representation, the probability value of each candidate semantic identifier is calculated and output through a classification output layer. By using graph structure modeling, the model can capture high-order transition paths and global topological features that are difficult for traditional sequence models to perceive, providing more accurate probabilistic predictions in scenarios where the transition relationships between semantic identifiers present a complex network structure.

[0139] In the embodiments of this specification, the pre-trained prediction model includes at least one of a lightweight baseline model, a recurrent neural network model, a self-attention regression model, and a graph neural network model. This enables the prediction framework to support flexible access to multiple model types and allows for the selection of appropriate models based on the accuracy requirements and computational resource constraints of the business scenario. The lightweight baseline model determines the state transition probability based on the jump frequency, enabling the prediction process to be executed quickly without training, providing efficient baseline prediction capabilities for resource-constrained scenarios. The recurrent neural network model and the self-attention regression model map semantic identifiers into continuous vector representations for sequence encoding, enabling the model to automatically learn temporal dependency patterns and long-range correlation information in historical sequences, improving the prediction accuracy of complex interest evolution patterns. The graph neural network model constructs a directed graph based on the historical jump relationships between user clusters for modeling, enabling the prediction process to incorporate global transfer topology information among user groups, improving the prediction capability of multi-path transfer patterns.

[0140] In one optional embodiment of this specification, the future time period includes multiple time periods; Inputting historical semantic identifier sequences into a pre-trained prediction model outputs the semantic identifier probability distribution for future time periods, including: Input the historical semantic identifier sequence into the pre-trained prediction model, and output the probability distribution of the next semantic identifier in the next future time period; The next predicted semantic identifier is determined based on the probability distribution of the next semantic identifier, and the next predicted semantic identifier is appended to the historical semantic identifier sequence to obtain the updated historical semantic identifier sequence. The updated historical semantic identifier sequence is input into the prediction model, and the semantic identifier probability distribution for multiple future time periods is output iteratively.

[0141] The next semantic identifier probability distribution is a codebook space probability vector output by the prediction model for the next future time period immediately adjacent to the current time. It represents the probability distribution of the target user's affiliation to various candidate semantic identifiers within the most recent prediction step. Specifically, the next semantic identifier probability distribution can include various output forms such as the original probability distribution obtained directly through single-step forward inference, the smoothed probability distribution adjusted by a temperature coefficient, and the conditional probability distribution truncated by a confidence threshold. The next semantic identifier probability distribution can serve as the starting output and foundation for multi-step autoregressive iterative prediction, and its prediction accuracy directly determines the input quality of subsequent iteration steps and the reliability of long-term prediction results. Furthermore, the next semantic identifier probability distribution corresponds to the shortest prediction time span, has the highest prediction accuracy and the lowest cumulative error among all prediction steps, and the concentration of its probability distribution reflects the model's level of certainty in judging the target user's recent interest migration direction.

[0142] The next predictive semantic identifier is a semantic identifier code determined from the probability distribution of next predictive semantic identifiers, representing the most likely affiliation of the target user in the most recent future time period. It serves as a predicted observation appended to the input sequence in autoregressive iterative prediction. Specifically, the next predictive semantic identifier can be selected in various ways, including a single identifier with the highest probability value in the probability distribution, an identifier randomly sampled from the probability distribution using a sampling strategy, and a high-confidence identifier determined after filtering by a confidence threshold. The next predictive semantic identifier can serve as an information transmission medium connecting adjacent prediction steps. By appending it to the end of the historical sequence, the model can make predictions for a longer time window in the next iteration based on the expanded sequence. Furthermore, the selection strategy of the next predictive semantic identifier has a decisive impact on the cumulative error propagation of multi-step iterative prediction. Selecting the maximum probability as a deterministic prediction can maintain the uniqueness and reproducibility of the iterative path, while random sampling based on the probability distribution can generate multiple possible interest evolution paths to assess the range of prediction uncertainty.

[0143] The updated historical semantic tag sequence is an extended sequence obtained by appending the next prediction semantic tag to the end of the original historical semantic tag sequence. It serves as the input data for the next iteration of prediction, carrying a state record spanning a longer time span, including prediction information. Specifically, the updated historical semantic tag sequence can include various maintenance forms such as a single-step extended sequence formed by appending a single prediction tag to the end of the original sequence, a multi-step extended sequence formed by appending multiple prediction tags after multiple iterations, and a fixed-length update sequence that uses a sliding window strategy to add new tags while removing the earliest tag. The updated historical semantic tag sequence allows the model to perceive all sequence information from the earliest historical observation to the latest prediction result in each subsequent iteration's prediction step. Furthermore, the updated historical semantic tag sequence contains both actual observations and model predictions. As the number of iterations increases, the proportion of predicted values ​​in the sequence gradually increases, and the overall information quality of the sequence shows a decreasing trend, requiring a reasonable trade-off between iteration depth and prediction reliability.

[0144] In practical applications, the specific execution process of inputting historical semantic identifier sequences into a pre-trained prediction model and outputting the probability distribution of the next semantic identifier in the next future time period can be found in the aforementioned embodiments, and will not be repeated here.

[0145] Once the probability distribution of the next semantic identifier is obtained, the next predicted semantic identifier can be determined based on the probability distribution of the next semantic identifier, and the next predicted semantic identifier can be appended to the historical semantic identifier sequence to obtain the updated historical semantic identifier sequence.

[0146] One alternative approach is to select the candidate semantic identifier with the highest probability value from the next semantic identifier probability distribution as the next predicted semantic identifier. This predicted identifier is then encoded and appended to the end of the historical semantic identifier sequence in chronological order. Simultaneously, the sequence length count and the latest time step marker are updated to obtain an updated historical semantic identifier sequence that extends the original sequence by one time step. Based on the deterministic nature of the maximum value selection strategy, the updated sequence generated in each iteration is unique and reproducible, facilitating the auditing of prediction results and the comparative evaluation of the strategy's effectiveness.

[0147] Another alternative approach is to perform a temperature-parameterized random sampling operation based on the probability distribution of the next semantic identifier. The next predicted semantic identifier is obtained by weighted random sampling from the probability distribution according to normalized probability values. This sampled identifier is then appended to the end of the historical sequence to form an updated historical semantic identifier sequence. Multiple independent samplings generate multiple differentiated update sequences to assess the uncertainty distribution of the predicted path. Through this random sampling strategy, the system can generate a prediction set covering multiple possible interest evolution paths, providing probabilistic multi-path reference information for downstream decision-making rather than a single deterministic judgment.

[0148] Another alternative approach is to perform a rationality check on the predicted semantic identifier after determining the next one but before performing the sequence append operation. This check determines whether the jump amplitude between the predicted identifier and the most recent identifiers in the historical sequence exceeds a preset reasonable range. If the jump amplitude is abnormal, the candidate identifier with the second highest probability is used as the replacement. The sequence append operation is then performed based on the verified predicted identifier to obtain the updated historical semantic identifier sequence. This rationality check mechanism effectively filters out unreasonable jump predictions caused by occasional model biases, improving the quality of predicted values ​​in the updated sequence and the stability of subsequent iterations.

[0149] In addition, a sliding window strategy can be used to maintain the updated historical semantic identifier sequence. While appending the next predicted semantic identifier to the end of the sequence, one or more of the earliest historical identifiers at the beginning of the sequence are removed, so that the updated sequence maintains a fixed-length window. Based on the fixed-length sequence input, the problem of excessively long sequences and increasing computational resource consumption caused by the increase of iteration steps is avoided, and a balance is maintained between information timeliness and computational efficiency.

[0150] Once the updated historical semantic identifier sequence is obtained, it can be input into the prediction model to iteratively output the semantic identifier probability distribution over multiple time periods.

[0151] Specifically, the updated historical semantic identifier sequence can be re-input into the prediction model, outputting the semantic identifier probability distribution for the next future time period. Predicted semantic identifiers are then determined from this probability distribution and appended to the end of the sequence. This process of prediction, determination, and appending is repeated until the cumulative number of output probability distributions reaches the total number of time periods included in the future time period, thus obtaining the semantic identifier probability distributions for each time period within multiple time periods. This approach progressively expands the prediction window in an autoregressive manner, with each prediction step based on a complete sequence containing the results of previous predictions, enabling long-distance predictions to utilize inference information from intermediate steps as supplementary context.

[0152] Optionally, an iteration termination condition can be preset for exiting the iteration execution. For example, after each iteration outputs the semantic identifier probability distribution, it can be determined whether the current iteration count has reached the preset step limit, or whether the maximum probability value in the output probability distribution is lower than a preset confidence threshold. If either termination condition is met, the iteration stops, and the semantic identifier probability distributions of all time periods obtained are taken as the final output.

[0153] In addition, a fixed-step iteration strategy can be adopted, which predetermines the number of iterations according to business needs. In each iteration, the semantic identifier probability distribution and the corresponding maximum probability value of each time period are recorded synchronously. After the iteration is completed, the prediction results of all time periods are output together with the confidence information, providing a multi-time period prediction view with confidence label for downstream business decisions.

[0154] In the embodiments of this specification, the prediction of future time periods is decomposed into a step-by-step iterative process, with each prediction step executed based on a sequence containing the latest information, providing a progressively advancing computational path for multi-time period predictions; by determining the next predicted semantic identifier based on the probability distribution of the next semantic identifier and appending it to the historical semantic identifier sequence, the results of previous predictions are incorporated into the input information of subsequent predictions, providing intermediate context supplementation for predictions of long-distance time periods; by iteratively outputting the probability distribution of semantic identifiers within multiple time periods, the prediction coverage is expanded from a single time period to multiple time periods, providing multi-step prediction data support for medium- and long-term interest prediction and forward-looking operational strategy formulation.

[0155] In one optional embodiment of this specification, determining the predicted semantic identifier based on the semantic identifier probability distribution includes: Based on the confidence level of each semantic identifier in the semantic identifier probability distribution and the preset confidence threshold, a prediction candidate set is determined from each semantic identifier; Based on the predicted candidate set, the predicted semantic identifier is determined.

[0156] Confidence score is the predicted probability value corresponding to each candidate semantic identifier in the semantic identifier probability distribution. It quantitatively reflects the degree of certainty of the prediction model that a particular candidate semantic identifier is the true next-hop identifier for the target user. Specifically, confidence score can include various numerical expressions such as the original probability value obtained directly after normalization from the prediction model's output layer, the corrected probability value after temperature scaling calibration, and the weighted confidence score dynamically adjusted based on historical prediction accuracy. Confidence score can serve as a core criterion for selecting the prediction candidate set. By comparing the confidence score of each candidate semantic identifier with a pre-set confidence threshold, predictions with high confidence can be separated from all candidates. Furthermore, the level of confidence score not only reflects the model's certainty about a single prediction result but also indirectly reflects the stability and predictability of the target user's current interest state. High confidence score usually means that the user's interest migration direction is clear and the model has a sufficient grasp of that migration direction.

[0157] A preset reliability threshold is a critical confidence level used to trigger an intervention strategy. It sets the minimum level of confidence required to incorporate the prediction result into the subsequent recommended intervention process. Specifically, the preset reliability threshold can be set in various ways, including a fixed numerical threshold determined based on the results of accuracy index analysis, multi-level thresholds configured differently according to different business scenarios, and dynamic thresholds that are periodically calibrated based on recent prediction accuracy feedback.

[0158] Furthermore, a pre-set confidence threshold can serve as a boundary standard distinguishing between high-confidence predictions and low-confidence predictions. Subsequent intervention strategies are only implemented for the corresponding prediction results when the maximum probability value in the probability distribution reaches this threshold. Setting the pre-set confidence threshold requires a trade-off between intervention coverage and intervention accuracy. A higher threshold ensures that the intervention strategy only applies to high-confidence predictions, thus reducing the loss of recommendation effectiveness due to misjudgment interventions. A lower threshold, on the other hand, can expand the coverage of the intervention strategy, allowing more users to receive proactive recommendation services.

[0159] The prediction candidate set is a subset of identifiers that meet the confidence requirements selected from all candidate semantic identifiers covered by the semantic identifier probability distribution. It serves as the direct object of subsequent operations to determine the predicted semantic identifiers. Specifically, the prediction candidate set can include various composition forms such as a set of units containing only a single identifier with the highest confidence, a multi-element set containing multiple identifiers whose confidence exceeds the threshold, and an empty set formed when the confidence of all candidate identifiers is below the threshold.

[0160] The prediction candidate set can serve as an intermediate filtering layer between the connection probability distribution output and the final prediction label determination, replacing the fixed-number Top-K filtering method with a threshold-based dynamic filtering method, allowing the number of candidates to adaptively change with the confidence distribution of the prediction results. Furthermore, the size of the prediction candidate set can reflect the model's overall grasp of the current prediction task. If the prediction candidate set contains multiple labels, it indicates the existence of multiple credible transfer directions; if the prediction candidate set is empty, a degradation processing strategy is triggered to ensure the continuity of the recommendation process.

[0161] In practical applications, the prediction candidate set can be determined from each semantic identifier based on the confidence level of each semantic identifier in the semantic identifier probability distribution and the preset confidence threshold, which can be achieved through various methods.

[0162] One alternative approach is to iterate through all candidate semantic identifiers in the semantic identifier probability distribution, read the confidence score value of each candidate identifier one by one, and compare it with a preset confidence threshold. All candidate identifiers with a confidence score greater than or equal to the preset threshold are included in the prediction candidate set. Based on business requirements, the identifiers in the prediction candidate set are then sorted in descending order of confidence score, forming a priority-ranked prediction candidate set. This one-by-one comparison screening method ensures that all candidate identifiers meeting the confidence score requirements are included in the prediction candidate set, avoiding the omission of any high-confidence prediction results, and the screening process is deterministic and reproducible.

[0163] Another alternative approach is to first sort all candidate identifiers in the semantic identifier probability distribution in descending order of confidence. Starting with the candidate identifier with the highest confidence, compare them sequentially with a pre-set confidence threshold. The filtering operation terminates when the first candidate identifier with a confidence level below the pre-set threshold is encountered, and all identifiers that have passed the comparison for that identifier are determined as the prediction candidate set. This sorted and truncated filtering method significantly reduces unnecessary comparison operations when the number of candidate identifiers is large, improving the efficiency of determining the prediction candidate set. It is particularly suitable for recommendation scenarios with large codebook space and high online real-time requirements.

[0164] In addition, a dynamic threshold calibration mechanism can be introduced to periodically adjust the preset reliability threshold based on the actual accuracy feedback of the prediction results within the recent preset time window. When the recent prediction accuracy is consistently higher than the expected level, the threshold can be appropriately lowered to expand the coverage capacity of the prediction candidate set. When the recent prediction accuracy shows a downward trend, the threshold can be appropriately raised to tighten the screening criteria. Through the closed-loop feedback between the threshold and prediction performance, the long-term stability of the prediction candidate set quality can be maintained.

[0165] Once the prediction candidate set is determined, the predicted semantic identifier can be determined based on the prediction candidate set.

[0166] One alternative approach is to directly select the semantic identifier with the highest confidence ranking in the prediction candidate set as the predicted semantic identifier, provided the prediction candidate set is not empty. The probability distribution output corresponding to this identifier is then used as the interest state encoding most likely to belong to the target user in the future time period, driving the matching of subsequent recommendation content and the execution of intervention strategies. By using the maximum value selection strategy, a unique prediction result can be determined from the prediction candidate set with minimal computational overhead, making it suitable for online recommendation scenarios with high real-time requirements and a need for deterministic prediction output.

[0167] Another alternative approach is to conduct a multi-dimensional comprehensive evaluation of each candidate identifier in the prediction candidate set. This involves calculating a comprehensive score by combining factors such as confidence level, the global frequency of the identifier in historical transition relationships, and the historical preference of the target user's group for that identifier. Based on this comprehensive score, the identifier with the highest score is selected as the predicted semantic identifier from the prediction candidate set. This multi-factor comprehensive evaluation method enables a more reasonable distinction among multiple high-confidence candidate identifiers, avoiding suboptimal decisions that may result from relying solely on a single probability value.

[0168] Another alternative approach is to perform a secondary filtering of the prediction candidate set by combining the target user's current specific business scenario and real-time conversation context. Candidate identifiers that do not match the current scenario are eliminated, and the final predicted semantic identifier is determined based on the candidate identifiers after this secondary filtering. This makes the prediction results more closely match the user's current actual usage environment and immediate interest state. Through this scenario-aware secondary filtering mechanism, the matching degree between the predicted semantic identifier and the user's current context can be improved, reducing recommendation bias caused by generalized predictions that are detached from the scenario context.

[0169] In addition, a downgrade processing strategy can be triggered when the prediction candidate set is empty. The predicted semantic identifier is determined according to the preset downgrade rules. The downgrade rules may include not performing active intervention operations on the target user, using the statistical prediction results of the lightweight baseline model as the alternative output, or directly using the identifier with the highest confidence in the semantic identifier probability distribution as the predicted semantic identifier without being subject to threshold constraints. The downgrade processing ensures that the recommendation process still has continuity and stability when the model prediction is not confident enough.

[0170] In the embodiments of this specification, the prediction candidate set is determined based on the confidence level of each semantic identifier and a pre-set confidence threshold. This provides a quantifiable admission standard for the prediction result screening process, effectively filtering out low-reliability candidates and providing a prerequisite guarantee for the accuracy of the final prediction. By using a confidence threshold rather than a fixed number as the screening criterion, the size of the prediction candidate set is dynamically adapted to the model's prediction confidence level, avoiding the forced output of low-quality candidates when the model is uncertain. By determining the prediction semantic identifier based on the prediction candidate set, the final decision is made within a limited range of candidates that have undergone quality screening, reducing the decision space while improving the reliability of the output results. By introducing a pre-set confidence threshold as an intervention trigger condition, the system only performs subsequent processing when the model has sufficient confidence, reducing intervention losses caused by misjudgments due to low confidence.

[0171] In one optional embodiment of this specification, determining the target user's interest migration direction based on the semantic association between the predicted semantic identifier and semantic identifiers within multiple historical time periods includes: Based on the posterior behavioral data of the target users, determine the interest semantic features of the target user clusters corresponding to the predicted semantic identifiers; Based on the differences between the semantic features of interest and the semantic features of user clusters corresponding to semantic identifiers in multiple historical time periods, the direction of interest migration is determined.

[0172] Posterior behavioral data, after the predicted semantic identifiers are determined, is used to verify and confirm the actual behavioral statistics of the business semantics. It is obtained by collecting and statistically analyzing the real behavior of users within the user clusters corresponding to the predicted semantic identifiers. Specifically, posterior behavioral data can include various behavioral statistical indicators such as click-through rate, add-to-cart rate, viewing duration distribution, product dwell time, interaction frequency, conversion rate, and category preference distribution of users within a preset statistical period. Posterior behavioral data can serve as an objective basis for determining the business semantics of user clusters. By statistically summarizing the actual behavioral patterns of users within the cluster, it obtains a description of the cluster's interest tendencies at the business level, rather than relying on a pre-established fixed mapping table. Furthermore, due to the large number of semantic identifiers and their continuous evolution, it is impossible to pre-establish complete semantic mapping relationships for all identifiers. Semantic verification based on posterior behavioral data is practically feasible and scalable. Moreover, once the semantic mapping is determined, it can be reused continuously in subsequent prediction periods without repeating the mapping operation.

[0173] A target user cluster is the group of users corresponding to a predicted semantic identifier in the residual quantization codebook space. It is formed by the aggregation of users predicted to jump to that semantic identifier within the prediction time period. Specifically, target user clusters can include various business types such as user groups with specific product category consumption preferences, user groups with specific content browsing tendencies, user groups with specific active time period characteristics, and user groups at specific lifecycle stages. Target user clusters can serve as the endpoint reference object for interest migration direction analysis. By determining their interest semantic features and comparing them with user clusters corresponding to historical time periods, the direction of change in the target user's interest state can be characterized. Furthermore, the semantic identifier itself is a sequence structure of complex semantics, and each layer of encoding may correspond to different business semantic dimensions. When determining the interest semantic features of the target user cluster, only the target semantic layer related to the current prediction target needs to be focused on, without needing to perform a complete mapping of all encoding layers.

[0174] Interest semantic features are the feature descriptions of user clusters at the business semantic level, used to characterize the core interest preferences and typical behavioral tendencies exhibited by the user group within the cluster. Specifically, interest semantic features can include descriptions of various business semantic dimensions such as high live streaming viewing tendency, high purchase intention for shopping cards, deep browsing preference for short videos, consumption tendency for specific vertical categories, and high interactive social preferences. Interest semantic features can serve as the basic unit for comparing semantic differences between different user clusters. By comparing the interest semantic features of two clusters, the migration direction and magnitude of changes in user groups' interests can be determined. Furthermore, there may be partial semantic overlap between the interest semantic features of different user clusters. For example, two clusters may both contain a high live streaming viewing tendency, but differ in their specific sub-fields. One cluster may also include game live streaming semantics, while the other may include beauty live streaming semantics, etc. This partial overlap allows the determination of interest migration direction to distinguish between two different change modes: complete migration and partial fine-tuning.

[0175] In practical applications, determining the interest semantic features of target user clusters corresponding to predicted semantic identifiers based on the target users' posterior behavioral data can be achieved through various methods.

[0176] One alternative approach involves collecting and summarizing the actual behavioral data of all users within a target user cluster over a preset statistical period. This includes calculating the mean and quantile characteristics of key behavioral indicators such as click-through rate, add-to-cart rate, viewing time distribution, and conversion rate. Based on the statistical distribution of these indicators, the main interest tendencies of the cluster at the business semantic level can be summarized. Indicators significantly higher than the platform's overall average are identified as the cluster's interest semantic features. This statistical summarization of behavioral indicators allows for the objective extraction of the cluster's business semantic description from real user behavior, avoiding biases caused by subjective mapping.

[0177] Another alternative approach is to perform multi-dimensional clustering analysis on the behavioral data of users within the target user cluster. This identifies the dominant and secondary behavioral patterns within the cluster. Core interest semantic features are determined based on the dominant behavioral patterns, while additional interest semantic features are determined based on the secondary behavioral patterns. These core and additional features are then combined to form a complete semantic description of the cluster's interests. This multi-level clustering analysis captures the hierarchical nature of the interest structure within user clusters, resulting in a more refined and complete semantic feature description.

[0178] Another alternative approach is to compare the current behavioral statistics of the target user cluster with historical mapping records. If the cluster has already undergone semantic mapping in a historical prediction period, the historical mapping results can be directly reused as the current interest semantic features. A remapping process is only triggered when the behavioral indicators show significant changes compared to the historical mapping period. This incremental update strategy reduces the overhead of repetitive semantic mapping calculations. By reusing historical mappings, the timeliness of semantic features can be guaranteed while significantly reducing the repetitive workload of manual mapping.

[0179] In addition, the semantic interpretation and annotation of posterior behavioral data can be combined with the knowledge of experts in the business domain, and statistical indicators can be transformed into understandable business semantic description text. For example, the statistical feature that the distribution of viewing time is concentrated in the live channel and the average viewing time exceeds a certain duration can be interpreted as a high tendency to watch live broadcasts. The accuracy of interest semantic feature description and business interpretability can be improved with the help of expert knowledge.

[0180] Once the semantic features of interests are determined, the direction of interest migration can be determined based on the differences between the semantic features of interests and the semantic features of user clusters corresponding to semantic identifiers in multiple historical time periods.

[0181] One alternative approach is to compare the interest semantic features of the target user cluster corresponding to the predicted semantic identifier with the interest semantic features of user clusters corresponding to each semantic identifier across multiple historical time periods. This identifies the semantic dimensions that have been added or weakened compared to historical clusters. The direction of interest migration is determined based on the trends of these added and weakened dimensions, and the direction indicated by the added semantic dimensions is identified as the target direction of interest migration. By analyzing the increase and decrease of semantic dimensions, the specific changes and migration directions of user interests can be accurately depicted from a business semantic perspective.

[0182] Another alternative approach is to calculate semantic similarity between the semantic features of the target user clusters and the semantic features of each cluster across multiple historical time periods. The trend of similarity over time is used to determine whether the interest state is stabilizing or undergoing significant migration. If the predicted semantic identifier matches the semantic identifier of the most recent historical time period, the interest state is considered to be in a self-stable state. If they differ, the degree of migration is determined by the magnitude of the similarity decrease. Through similarity trend analysis, both the direction and intensity of interest migration can be obtained simultaneously, providing a quantitative reference for adjusting the strength of the recommendation strategy.

[0183] Another alternative approach is to encode the semantic features of each user cluster's interests into a structured vector representation. The direction and magnitude of interest migration are determined by calculating the difference vector between the predicted semantic identifier's corresponding vector and the corresponding vector for historical time periods. The sign and magnitude of each component in the difference vector determine which specific interest dimensions have been enhanced or weakened. The reliability of the migration direction is then weighted and evaluated by combining the state transition probabilities recorded in historical jump relationships. This vectorized difference calculation method transforms qualitative semantic comparisons into quantitative numerical operations, improving the computational efficiency and consistency of determining the direction of interest migration.

[0184] In addition, the interest semantic features corresponding to each semantic identifier in multiple historical time periods can be arranged in chronological order to form an interest evolution trajectory. The interest semantic features corresponding to the predicted semantic identifier can be used as the extended prediction points of the trajectory. The direction of interest migration can be determined by comprehensively considering the overall trend and local change trends of the trajectory. When the predicted semantic identifier is consistent with the historical trajectory direction, it is determined as a trend continuation. When the predicted semantic identifier deviates from the historical trajectory, it is determined as an interest mutation. The temporal coherence of migration direction determination can be improved through trajectory analysis.

[0185] In the embodiments of this specification, the interest semantic features of the clusters corresponding to the predicted semantic identifiers are determined based on the posterior behavioral data of the target users. This allows the business meaning of discrete identifiers to be dynamically verified through real behavioral data, providing a fact-based semantic input for determining the migration direction. By extracting interest semantic features at the user cluster level, the acquisition of semantic features is based on the statistical regularity of group behavior rather than individual accidental performance, improving the stability and representativeness of the migration direction determination. By determining the migration direction based on the difference in interest semantic features between the predicted semantic identifiers and semantic identifiers over historical time periods, the capture of interest changes is completed at the semantic level rather than relying on a simple comparison of identifier values, giving the migration direction an interpretable business meaning. By comparing the differences of semantic identifiers covering multiple historical time periods, the determination of the migration direction incorporates temporal evolution information, providing data support for distinguishing between short-term fluctuations and trend migrations.

[0186] In one optional embodiment of this specification, recommending target content corresponding to the direction of interest migration to the target user includes: Determine the target content recommendation strategy based on the target users' interest migration direction; Based on the target content recommendation strategy, target recommended content that corresponds to the direction of interest migration is recommended to the target users.

[0187] A target content recommendation strategy is a differentiated content distribution scheme determined based on the direction of interest migration. It serves as an intermediate decision-making layer between the migration direction and the specific recommendation action. Specifically, a target content recommendation strategy can include a combination of parameters across multiple dimensions, such as the scope of candidate content selection, content recommendation weight allocation, recommendation timing selection, and recommendation presentation style. These parameters collectively determine what content is presented to the target user, with what intensity, and in what scenario.

[0188] The target content recommendation strategy can leverage the evolving trends of user interests expressed by the interest migration direction, transforming the abstract migration direction into concrete distribution instructions that the recommendation system can directly execute. This allows the recommendation engine to complete content retrieval, ranking, and exposure according to the parameters specified by the strategy. Furthermore, the generation logic of the target content recommendation strategy is built upon semantic identification of business semantics. Each strategy can be traced back to a clear business meaning, enabling operations personnel to understand the decision-making basis behind the strategy and to manually review and intervene in the strategy based on business experience. This prevents the recommendation process from becoming an unexplainable black box decision-making process, ensuring the compliance and controllability of recommendation behavior.

[0189] In practical applications, determining the target content recommendation strategy based on the target user's interest migration direction can be achieved through various methods.

[0190] One alternative approach is to pre-establish a mapping configuration table between migration directions and recommendation strategies. Each interest migration direction is mapped to a defined set of recommendation operation parameters. After obtaining the target user's interest migration direction, the corresponding strategy configuration item is retrieved from the mapping configuration table using the migration direction as the search key. This retrieved strategy configuration item is then used as the target content recommendation strategy. This method simplifies the strategy determination process into a single deterministic mapping query operation, offering high execution efficiency and stable, reproducible results. It is suitable for business scenarios with limited migration direction types and clearly defined strategy rules.

[0191] Another alternative approach is to further combine the target user's current value level information with the interest migration direction obtained after acquiring that information. The strategy decision-making module then dynamically generates a target content recommendation strategy that matches the target user's current state by integrating the input from both migration direction and value level. This method allows the strategy determination process to consider both migration trends and existing user value, avoiding recommendation bias caused by relying solely on information from a single direction, and improving the matching accuracy between the strategy and the user's actual state.

[0192] Another alternative approach is to input migration direction information into a pre-trained strategy recommendation model. The model then outputs a recommendation strategy identifier based on the migration direction and associated user profile features. Based on this identifier, it loads the corresponding strategy execution plan from a strategy library and identifies the loaded plan as the target content recommendation strategy. This method is suitable for complex business scenarios with numerous migration direction types and a large strategy combination space. It leverages the model's non-linear fitting capabilities to discover strategy matching relationships that are difficult to cover with manual rules.

[0193] In addition, operations personnel can manually specify recommended strategies for specific migration directions based on their business experience, which is suitable for the transition phase when the strategy rules have not yet been fully established in the early stages of a new business launch.

[0194] Once the target content recommendation strategy is determined, target content that corresponds to the direction of interest migration can be recommended to the target users based on the target content recommendation strategy.

[0195] One alternative approach is to adjust the initial recommendation scores of each candidate content in the candidate content set according to the recommendation weight adjustment parameters specified in the target content recommendation strategy. The weighted candidate content is then sorted from highest to lowest score, and a predetermined number of the top-ranked candidate content are selected as the target recommendation content and pushed to the target user's content display interface. This method directly changes the content ranking result through strategy-driven weight adjustment, ensuring that the recommendation output aligns with the direction of interest migration. The execution logic is simple, and the cost of modifying the existing recommendation chain is relatively low.

[0196] Another alternative approach is to select a subset of candidate content from the full content library that meets preset conditions regarding semantic relevance to the interest migration direction, based on the candidate pool filtering rules specified in the target content recommendation strategy. Then, sorting and selection operations are performed within this subset, and the selected result is determined as the target recommendation content. This method constrains the source range of candidate content at the strategy level, ensuring semantic consistency between the recommended content and the migration direction from the outset, and reducing the consumption of recommendation resources by irrelevant content.

[0197] Another alternative approach is to present the target recommended content in a specific location and format within the target user's content consumption interface, based on the content exposure priority rules specified in the target content recommendation strategy. This gives content corresponding to the direction of interest migration higher exposure weight and visual salience. This method extends the strategy to the content display stage, enhancing the recommendation effect through differentiated processing at the front-end presentation level, and increasing the target user's perception probability and willingness to interact with content in the migration direction.

[0198] In addition, the target content recommendation strategy can be sent to the execution layer of the recommendation engine in the form of message instructions, and the execution layer can complete the entire chain operation of content retrieval, sorting and delivery according to the instructions.

[0199] In the embodiments described in this specification, target recommended content is recommended to target users based on a target content recommendation strategy. This ensures that the decision intent of the strategy layer is accurately transmitted to the recommendation execution stage, guaranteeing the consistency between the recommendation output and the strategy objective. By recommending content that corresponds to the direction of interest migration, the recommended content received by the user matches the trend of their changing interests, improving the matching degree of the recommended content with the user's current needs. By executing recommendation operations with a strategy as the driving force, the recommendation process has a traceable decision-making link, providing a basis for attribution analysis of recommendation effects and strategy iteration. Through strategy-based recommendation execution, a complete closed loop is achieved from the user's perception of interest migration to the precise delivery of personalized content.

[0200] In one optional embodiment of this specification, the direction of interest migration includes upward migration, downward migration, and stable invariance; Based on the target users' interest migration direction, determine the target content recommendation strategy, including: When the direction of interest migration is upward migration, the target content recommendation strategy is determined to be a conversion acceleration strategy; When the direction of interest migration is downward migration, the target content recommendation strategy is determined to be a churn retention strategy; Assuming that the direction of interest migration remains stable, the recommended content strategy is determined to be a strategy of maintaining supply.

[0201] Ascending migration is a type of interest evolution in which users move from lower-value semantic tag clusters to higher-value semantic tag clusters, reflecting an increase in the value of user interests. Specifically, ascending migration can manifest as a change in state where a user currently belongs to a non-high-value semantic tag cluster, but predicts that they will belong to a high-value semantic tag cluster in the next moment. For example, in e-commerce scenarios, a user's evolution from a low-value cluster where they only browse but do not place an order to a high-value cluster where they have a higher tendency to place an order constitutes a typical ascending migration.

[0202] Furthermore, upward migration can indicate that users are in a critical window of interest cultivation, their willingness to consume in this business area is increasing, and they are potential high-value conversion targets. The determination of upward migration is closely related to the value hierarchy classification in specific business scenarios. The classification of high-value semantic tag clusters is based on business indicators. For example, in e-commerce scenarios, clusters with high order rates are classified as high-value clusters, while clusters that only browse without purchasing are classified as low-value clusters. The classification of value hierarchy varies depending on the business scenario.

[0203] Descending migration is a type of interest evolution in which users migrate from higher-value semantic tag clusters to lower-value semantic tag clusters, reflecting a decline in the value of user interests. Specifically, descending migration can manifest as a change in state where a user currently belongs to a high-value semantic tag cluster, but is predicted to belong to a non-high-value semantic tag cluster in the next moment. For example, in e-commerce scenarios, a user sliding from a high-value cluster with a high probability of placing an order to a low-value cluster where they only browse without placing an order constitutes a typical descending migration.

[0204] Furthermore, descent migration indicates that a user is on the verge of losing interest, meaning their willingness to consume in that business area is weakening. Without timely intervention, this could lead to complete user churn. Identifying descent migration allows recommendation systems to intervene before user interest completely fades, slowing the decline through targeted adjustments to content offerings. This buys time to recover user value and serves as a crucial early warning signal in user lifecycle management.

[0205] Stability and invariance refer to the type of interest maintenance where a user's semantic identifier cluster affiliation remains unchanged within adjacent prediction periods, reflecting that the user's interest state is in a self-loop state. Specifically, stability and invariance can manifest as a high-value self-loop situation where the user currently belongs to a high-value semantic identifier cluster and is predicted to still belong to that high-value semantic identifier cluster in the next time step, or as a non-high-value self-loop situation where the user currently belongs to a non-high-value semantic identifier cluster and is predicted to still belong to that non-high-value semantic identifier cluster in the next time step.

[0206] Furthermore, stability indicates that user interests and preferences have stabilized, and users maintain a continuous and stable level of engagement in the current business area, representing the main composition of the platform's existing user base. For high-value self-loop users, stability means that users have formed solid consumption habits, and the recommendation system should focus on the consistency and stability of the recommendation experience, avoiding excessive adjustments to recommendation strategies that might interfere with users' existing interests. For non-high-value self-loop users, stability means that users have not yet shown obvious signs of interest shifts, and the recommendation system can distribute content according to conventional recommendation logic.

[0207] The conversion acceleration strategy is a recommendation strategy designed for users transitioning to higher-value content. Its core approach is to proactively enhance the supply of high-conversion content to accelerate the user's interest migration process. Specifically, this strategy can include measures such as increasing the recommendation weight of high-conversion category content, expanding the coverage of the high-conversion candidate content pool, and increasing the recommendation frequency of conversion-guiding content. This allows users to be exposed to key content that drives conversion more intensively during the interest growth phase. The conversion acceleration strategy intervenes before the user has completed the migration, shortening the time cycle from initial interest to actual conversion through proactive enhancement of high-conversion content supply. Furthermore, the core idea of ​​the conversion acceleration strategy is to seize the window of opportunity when user interest is rising, rapidly converting potential interests into actual consumption behavior through proactive content enhancement. This enables users to quickly transition from low-value to high-value levels, thereby improving user lifetime value and the platform's overall conversion efficiency.

[0208] Churn retention strategies are recommendation strategies designed for users experiencing a decline in interest, with the core approach of maintaining a consistent supply of content to slow down the fading of interest. The aim is to retain users and prevent further churn. Specifically, churn retention strategies may include maintaining a stable supply of content in categories the user has historically preferred, moderately increasing the exposure of content types the user previously consumed frequently, and reducing the impact of novel content on the user's existing interests. These measures maintain user stickiness to the platform through the continuity of content supply. Churn retention strategies can intervene when user interest shows signs of decline but has not yet completely disappeared. Targeted content provision can evoke the user's existing interest in the platform, slowing down the rate of interest decline. Furthermore, the core idea of ​​churn retention strategies is to counteract the natural decline in user interest with the stability of content supply, extending the user's active period as much as possible within the window before interest completely fades, preserving the possibility of recovering user value and a subsequent resurgence of interest.

[0209] The supply maintenance strategy is a recommendation strategy designed for stable users, with the core objective of maintaining the continuity of the current recommendation strategy. Its aim is to preserve users' existing stable interests. Specifically, the supply maintenance strategy may include measures such as continuing the user's current recommendation parameter configuration, maintaining the stability of the candidate content pool, and avoiding significant fluctuations in the recommendation strategy. This ensures that users consistently receive recommended content that matches their existing interests during the stable period. The supply maintenance strategy ensures that the recommendation system does not make unnecessary strategy adjustments for users while maintaining stable user interests, avoiding the negative impact of frequent strategy switching on user experience. Furthermore, the core idea of ​​the supply maintenance strategy is to respect users' established stable interests and preferences, consolidating their existing consumption habits through the continuity and stability of the recommendation strategy. This allows high-value, stable users to maintain their value level in the long term, contributing continuous and stable business revenue to the platform.

[0210] In practical applications, the target content recommendation strategy is determined based on the target user's interest migration direction. The appropriate strategy determination method can be adopted according to the specific type of migration direction.

[0211] One possible approach is to determine, when the interest migration direction is upward migration, whether the target user currently belongs to a non-high-value semantic tag cluster and predict that they will belong to a high-value semantic tag cluster in the next moment. If both conditions are met, the target content recommendation strategy is determined to be a conversion acceleration strategy, and the supply of high-conversion content is strengthened in advance based on this strategy. By implementing the conversion acceleration strategy, the recommendation system can increase the recommendation weight of high-conversion category content, expand the coverage of the high-conversion candidate content pool, and enable target users to be intensively exposed to key content that promotes conversion during the interest rise period. Based on the value level divided by semantic tag business semantics, such as using the order rate as the basis for dividing high-value clusters in e-commerce scenarios, the system can accurately identify potential high-value users in the upward migration process and accelerate their leap from low-value to high-value levels.

[0212] Another alternative approach, when the interest migration direction is downward migration, is to determine whether the target user currently belongs to a high-value semantic tag cluster and predict whether they will belong to a non-high-value semantic tag cluster in the next moment. If both conditions are met, the target content recommendation strategy is determined to be a churn retention strategy, and the decline in user interest is slowed down by maintaining content supply. Through the execution of the churn retention strategy, the recommendation system can maintain a stable supply of content in categories that the user has historically preferred, appropriately increase the exposure of content types that the user has previously consumed frequently, and evoke the user's existing interest memories on the platform. Based on the supply maintenance parameters configured in the strategy, the system can intervene in a timely manner during the window period when user interest shows signs of decline but has not yet completely churned, slowing down the speed at which the user slides from a high-value level to a low-value level, and buying valuable time to recover user value.

[0213] Another alternative approach, assuming the direction of interest migration remains stable, is to determine whether the target user remains within a self-loop state of the same semantic identifier cluster within adjacent prediction periods. If the user is in a self-loop state of a high-value cluster, the target content recommendation strategy is set to a maintenance supply strategy, ensuring the continuity and stability of the current recommendation strategy. If the user is in a self-loop state of a non-high-value cluster, content distribution is performed according to the conventional recommendation logic. By maintaining the execution of the maintenance supply strategy, the recommendation system can continue the user's current recommendation parameter configuration, maintain the stability of the candidate content pool, and avoid significant fluctuations in the recommendation strategy that could disrupt the user experience. Based on the persistence and stability of user interests under stable conditions, the system can consolidate the existing consumption habits of high-value, stable users, ensuring they maintain a high-value level in the long term and contributing continuous and stable business revenue to the platform.

[0214] Furthermore, a fallback strategy mechanism can be introduced. When the confidence level of the interest migration direction determination is insufficient or an uncovered migration type occurs, a preset default recommendation strategy is used as a fallback solution to ensure that the recommendation system can still complete content distribution normally under abnormal circumstances. By introducing a fallback strategy, the recommendation system can avoid recommendation interruptions caused by failed migration direction determination or missing strategy mapping, ensuring the continuity of user experience.

[0215] In the embodiments of this specification, interest migration direction is subdivided into three types: upward migration, downward migration, and stable / unchanged, corresponding to conversion acceleration strategy, churn retention strategy, and supply maintenance strategy, respectively. This enables the recommendation system to execute differentiated content distribution schemes based on different evolution trends of user interest value, significantly improving the matching accuracy between recommendation strategies and users' actual states. At the same time, through the value hierarchy division and strategy mapping logic based on semantic identification of business semantics, operators can clearly understand the business meaning of each strategy decision, ensuring the interpretability and auditability of recommendation decisions, and realizing the upgrade of the recommendation system from passive response to proactive operation.

[0216] In one optional embodiment of this specification, based on a target content recommendation strategy, target recommended content corresponding to the direction of interest migration is recommended to the target user, including: When the target content recommendation strategy is a conversion acceleration strategy, determine the target user clusters corresponding to the predicted semantic tags; Increase the recommendation weight of content types that have a positive correlation with the target user cluster; Based on the improved recommendation weight, target content that corresponds to the direction of interest migration is recommended to the target user.

[0217] Positive correlation is the positive relationship between recommended content types and user clusters, used to characterize that users within a user cluster have a higher consumption tendency for a specific recommended content type than the platform's overall level. Specifically, positive correlation can include correlation measures quantified based on consumer behavior indicators such as click-through rate, conversion rate, dwell time, and interaction frequency. If a user cluster's consumption indicators for a specific recommended content type are significantly higher than the platform average, a positive correlation can be determined between that recommended content type and that cluster. Positive correlation can be pre-calculated and stored as a correlation table through offline statistics, or dynamically updated based on real-time feedback data through online learning. Furthermore, positive correlation can serve as a basis for adjusting content supply-side weights, enabling the recommendation system to target and enhance the exposure of relevant content types based on the consumption preferences of different user clusters, thereby improving the matching degree between recommended content and the interests of the target user group.

[0218] Recommended content types are categories of distributable content formats on the platform, used to classify massive amounts of content resources according to their presentation and interaction methods. Specifically, recommended content types can include various formats such as live streams, short videos, images and text, and product cards. Each type can be further subdivided into several subtypes based on presentation format, interaction method, and commercial attributes. Recommended content types serve as the basic operational unit for weight adjustment and exposure allocation in the recommendation system, with each type corresponding to a set of configurable exposure parameters. Furthermore, recommended content types are represented in the recommendation system using parameterized identifiers, facilitating the system's statistical analysis of consumer behavior indicators, calculation of correlations, and execution of differentiated weight control strategies based on type.

[0219] Recommendation weight is a parameter that controls the exposure ratio of various types of content in the recommendation results, used to adjust the display share of different content types in the final recommendation list presented to the user. Specifically, recommendation weight can include one or more of the following: a type weighting coefficient acting on the recall stage, a scoring weighting factor acting on the ranking stage, and an exposure quota parameter acting on the re-ranking stage. Recommendation weight can influence the probability of a candidate's content being viewed and consumed by the user by changing its position in the ranking list; increasing the recommendation weight of a certain type means increasing the exposure opportunities for that type of content. Furthermore, recommendation weight can be implemented by incrementally adjusting a base weight, and weight caps and adjustment step sizes can be set to prevent excessive exposure of a single type of content, which could impair the diversity of the recommendation results.

[0220] In practical applications, when the target content recommendation strategy is a conversion acceleration strategy, the target user clusters corresponding to the predicted semantic identifiers can be determined through various methods.

[0221] One alternative approach is to determine the target user cluster based on the mapping relationship between semantic identifiers and user clusters. Specifically, a predicted semantic identifier for the target user can be obtained. This predicted semantic identifier representation model predicts the semantic identifier to which the target user will belong in the next snapshot. Based on a pre-built mapping table between semantic identifiers and user clusters, the user cluster mapped to the predicted semantic identifier is queried, and the queried user cluster is identified as the target user cluster. This target user cluster belongs to a high-value cluster group, meaning the user cluster corresponding to the predicted semantic identifier is located within the high-value cluster set.

[0222] Another alternative approach is to determine the target user cluster by calculating the semantic distance between the predicted semantic identifier and the centers of each candidate cluster. Specifically, the cluster center vectors of each user cluster can be obtained, the distance metric between the semantic vector corresponding to the predicted semantic identifier and the center vectors of each cluster can be calculated, and the candidate cluster with the smallest distance metric and belonging to the high-value cluster group can be selected as the target user cluster. Through quantitative comparison of semantic distance, it can be ensured that the determined target user cluster has the highest semantic consistency with the interest direction represented by the predicted semantic identifier.

[0223] Another alternative approach is to determine target user clusters based on the historical migration trajectories of target users and the transfer probability distribution of each cluster. Specifically, the actual transfer target distribution of historical user groups with similar predicted semantic identifiers between different time snapshots can be statistically analyzed. Based on this transfer target distribution, the transfer probability of each candidate cluster can be calculated, and the candidate cluster with the highest transfer probability and belonging to the high-value group can be identified as the target user cluster. Statistical analysis of migration patterns at the group level can improve the accuracy and robustness of target user cluster determination.

[0224] Furthermore, a comprehensive determination can be made by combining multiple candidate clusters corresponding to the predicted semantic identifiers. Specifically, when the prediction model outputs multiple candidate predicted semantic identifiers, the candidate user clusters corresponding to each candidate predicted semantic identifier can be determined separately. The prediction confidence of each candidate user cluster is then weighted and fused, and the candidate user cluster with the highest fused confidence is determined as the target user cluster. This multi-candidate fusion approach can reduce the impact of a single prediction result bias on the determination of the target user cluster.

[0225] Once the target user cluster is identified, the recommendation weight of recommended content types that have a positive correlation with the target user cluster can be increased.

[0226] One alternative approach is to filter recommended content types with positive correlations based on a relationship table and apply incremental weights. Specifically, a pre-built relationship table between clusters and recommended content types can be queried to obtain one or more recommended content types with positive correlations to the target user cluster. A preset weight increment is then added to the base weight of these one or more recommended content types to obtain the increased recommendation weight. By combining table lookup and incremental weighting, targeted adjustments to recommendation weights can be quickly achieved.

[0227] Another alternative approach is to quantify the strength of the positive correlation by statistically analyzing the consumption behavior metrics of users within the target user cluster for each type of recommended content, and then adjust the recommendation weights accordingly. Specifically, the click-through rate (CTR) and conversion rate (CTR) for each type of recommended content within the target user cluster can be calculated. These CTRs and CTRs are then compared to the platform's overall average. Recommended content types that significantly exceed the platform average are identified as having a positive correlation, and the corresponding weight increase is determined based on the extent to which they exceed the platform average. By quantitatively calculating based on actual consumption behavior metrics, the weight adjustments can be aligned with the actual consumption preferences of the target user cluster.

[0228] Another alternative approach is to differentiate the recommendation weights based on the strength of the positive correlation. Specifically, a correlation strength coefficient can be calculated for each type of recommended content that has a positive correlation with the target user cluster. The weight amplification factor is then determined based on the magnitude of the correlation strength coefficient, applying a larger weight amplification factor to content types with higher correlation strength and a smaller weight amplification factor to content types with lower correlation strength. This differentiated weighting allows the granularity of the recommendation weight adjustment to be adapted to the actual strength of the positive correlation.

[0229] Furthermore, the recommendation weights can be increased with an upper limit, in line with the conversion acceleration strategy. Specifically, a weight cap can be set for each type of recommended content during the weight increase process. Once the increased recommendation weight reaches the cap, further increases can be stopped. Additional weight bonuses can be applied to recommended content types strongly correlated with high-value behaviors. The magnitude of the weight increase can be dynamically adjusted based on real-time click-through rate and conversion rate feedback. By setting weight caps and dynamic adjustment mechanisms, the overall diversity of recommendation results can be maintained while strengthening the supply of target content.

[0230] By increasing the recommendation weight, targeted content that corresponds to the direction of interest migration can be recommended to the target user based on the increased recommendation weight.

[0231] One alternative approach is to apply enhanced recommendation weights to content scoring and ranking during the recommendation ranking stage. Specifically, the enhanced recommendation weights can be used as type-weighted factors in the content scoring model. Each recommended content in the candidate content set is weighted and summed according to its enhanced recommendation weight. The candidate content is then ranked based on the comprehensive score obtained from the weighted sum, and the candidate content with the highest comprehensive score is output as the target recommended content to the recommendation results list. By applying weights during the ranking stage, the positional distribution of each type of content in the recommendation results can be altered.

[0232] Another alternative approach is to filter candidate content and distribute it with weights based on the target interest domain indicated by the interest migration direction. Specifically, the target interest domain indicated by the interest migration direction can be determined based on the predicted semantic identifier. Candidate content belonging to this target interest domain and corresponding to the recommended content type with increased weight can be filtered from the content pool. Within the filtered candidate content set, content is then sorted and distributed according to the increased recommendation weight. By filtering first and then weighting, the consistency between the recommended content and the interest migration direction can be ensured.

[0233] Another alternative approach is to control the exposure quota for each content type based on the increased recommendation weight through a traffic allocation mechanism. Specifically, the exposure proportion that each content type should receive in the recommendation results can be calculated based on the increased recommendation weight. Exposure quotas are then allocated to each content type according to this proportion, and the corresponding target content is distributed to target users within the constraints of these quotas. By controlling the exposure quotas, it is possible to ensure, from a total volume perspective, that the content type with the increased weight receives a corresponding share of exposure.

[0234] Furthermore, real-time feedback can be used to perform closed-loop adjustments to the recommendation process based on the increased weights. Specifically, during the distribution of targeted recommended content to target users, real-time consumption feedback data on various types of recommended content can be collected. The conversion effect of the increased recommendation weights can be evaluated based on this feedback data. If the conversion effect meets expectations, the current weight setting can be maintained; if the conversion effect does not meet expectations, the recommendation weights can be further increased or the coverage of targeted recommended content expanded. Through this closed-loop adjustment mechanism, the recommendation weights can continuously adapt to the conversion process of target users.

[0235] In the embodiments of this specification, by determining the target user clusters corresponding to the predicted semantic identifiers under the conversion acceleration strategy, it is possible to accurately locate the target interest clusters corresponding to the target user groups that are about to undergo value leaps, and to strengthen the content supply in the target interest direction based on cluster-level association analysis; by recommending target recommended content corresponding to the interest migration direction to target users based on the improved recommendation weight, the recommendation results are matched with the interest evolution direction of target users, and the content supply in the target interest direction is strengthened in advance before the target users actually complete the cluster jump, shortening the path for target users to transform from a non-high-value state to a high-value state, improving the fit between recommended content and target users' migration intentions, and promoting the transformation process of target users to high-value clusters.

[0236] In one optional embodiment of this specification, based on a target content recommendation strategy, target recommended content corresponding to the direction of interest migration is recommended to the target user, including: When the target content recommendation strategy is a churn retention strategy, determine the current user cluster to which the target user currently belongs; Maintain the recommendation weight of content types that have a positive correlation with the current user cluster, and reduce the recommendation weight of content types that have a negative correlation with the current user cluster; Based on the adjusted recommendation weights, target content that corresponds to the direction of interest migration is recommended to the target user.

[0237] The current user cluster is the user cluster corresponding to the semantic identifier to which the target user belongs in the current time period, used to represent the target user's current interest group. Specifically, the current user cluster can include user clusters mapped from the semantic identifier of the target user's current snapshot, which are composed of user groups with the same or similar semantic identifiers. The current user cluster can be determined by semantically encoding the target user's recent content interaction behavior sequence to obtain the corresponding current semantic identifier, and then querying the mapping relationship between the current semantic identifier and the user cluster. Furthermore, the current user cluster and the target user cluster corresponding to the predicted semantic identifier form a time-dimensional correspondence. Under the churn retention strategy, the current user cluster belongs to the high-value cluster group, that is, the user cluster to which the target user currently belongs is within the high-value cluster set, and is the benchmark cluster for maintaining content supply and weight adjustment in the future.

[0238] Inverse correlation is a negative correlation between recommended content types and user clusters. It characterizes a declining trend in the interaction tendency of a user group within a user cluster towards a specific recommended content type, or a positive correlation between interaction with that specific recommended content type and user churn. Specifically, inverse correlation can include negative correlation measures quantified based on the magnitude of the decrease in interaction frequency, the decay rate of interaction duration, and the correlation between interaction behavior and churn behavior. When the interaction frequency of a user group in a cluster towards a specific recommended content type continues to decline, or when interaction with that specific recommended content type is positively correlated with user churn, an inverse correlation can be determined between that recommended content type and that cluster. Inverse correlation can be obtained through joint statistical analysis of historical interaction behavior data and churn behavior data, and can be stored as a correlation table for use during weight adjustment. Furthermore, inverse correlation can serve as a basis for content supply-side weight suppression, enabling the recommendation system to reduce the exposure of content types that may distract user interests in user clusters at risk of churn, thereby maintaining the stability of target users' interest interactions within the current high-value clusters.

[0239] In practical applications, when the target content recommendation strategy is a churn retention strategy, the specific execution process for determining the current user cluster to which the target user currently belongs can be found in the aforementioned embodiments, and will not be repeated here.

[0240] Once the current user cluster is determined, the recommendation weight of content types that have a positive correlation with the current user cluster can be maintained, while the recommendation weight of content types that have a negative correlation with the current user cluster can be reduced.

[0241] One alternative approach is to identify recommended content types with positive and negative associations based on a relationship table and perform differentiated weighting operations. Specifically, a pre-built relationship table between clusters and recommended content types can be queried to obtain one or more recommended content types with positive associations and one or more recommended content types with negative associations to the current user cluster. The base weights of positively associated recommended content types are kept unchanged to maintain their exposure levels, while a preset weight decay is applied to negatively associated recommended content types on top of their base weights to reduce their exposure levels. Through table lookup and differentiated weighting operations, bidirectional adjustment of recommendation weights can be quickly completed.

[0242] Another alternative approach is to identify positive and negative associations by statistically analyzing the interaction trend indicators of users within the current user cluster towards each type of recommended content, and adjust the weights accordingly. Specifically, the interaction frequency trend of users within the current user cluster towards each type of recommended content can be calculated over a continuous time window. Recommended content types with stable or increasing interaction frequencies are identified as positive association types and their recommendation weights are maintained, while recommended content types with continuously decreasing interaction frequencies are identified as negative association types and their recommendation weights are reduced. Through dynamic identification based on interaction trend indicators, weight adjustments can be matched to the actual evolution of interests within the current user cluster.

[0243] Another alternative approach is to tieredly reduce recommendation weights based on the strength of the inverse correlation. Specifically, a negative correlation strength coefficient can be calculated for each type of recommended content that has an inverse correlation with the current user cluster. The weight decay factor is then determined based on the magnitude of the negative correlation strength coefficient. A larger weight decay is applied to recommended content types with a high negative correlation strength, a smaller weight decay is applied to recommended content types with a low negative correlation strength, and the weight of recommended content types with a positive correlation remains unchanged or is moderately increased. This tiered reduction approach ensures that the weight suppression is commensurate with the degree of inverse correlation.

[0244] Furthermore, the cluster-level weight adjustments can be modified by incorporating individual user preferences. Specifically, while maintaining or reducing the cluster-level recommendation weights, the content preference characteristics of individual target users can be obtained. The cluster-level weight adjustments can then be modified based on these individual content preference characteristics. If an individual target user shows a significant preference for a particular recommended content type whose weight has been reduced, the weight decay value can be appropriately adjusted back.

[0245] With the recommendation weights adjusted, target content corresponding to the direction of interest migration can be recommended to the target user based on the adjusted recommendation weights. The specific recommendation process can be found in the aforementioned embodiments, and will not be repeated here.

[0246] In the embodiments of this specification, by determining the current user cluster to which the target user currently belongs under the churn retention strategy, it is possible to accurately locate the high-value interest cluster currently occupied by the target user group that is about to experience value degradation. While maintaining the stability of the content supply in the current high-value interest direction, it reduces the exposure of content that may distract the target user's interests. By recommending target recommended content to the target user based on the adjusted recommendation weight and corresponding to the interest migration direction, it is possible to maintain the positive content supply while suppressing the exposure of negative content, stabilize the target user's interest interaction within the current high-value cluster, reduce the probability of the target user migrating from the current high-value cluster to a non-high-value cluster, and improve the retention effect of the target user within the high-value cluster.

[0247] In one optional embodiment of this specification, an embodiment of a clustering codebook update method is also provided. Specifically, see [link to relevant documentation]. Figure 2 , Figure 2 A flowchart illustrating a clustering codebook update method according to one embodiment of this specification is shown. Figure 2 As shown, this clustering codebook update method specifically includes the following steps: Step 202: Obtain the current user representation vector and the current cluster codebook, wherein the current cluster codebook is constructed based on the historical user representation vector, including multiple cluster centers divided according to the clustering level and the corresponding status identifiers; Step 204: Based on the distance between the current user representation vector and the center vectors of multiple cluster centers, determine the uncovered user representation vectors from the current user representation vectors; Step 206: Determine new cluster centers based on the uncovered user representation vectors; Step 208: Merge the newly added cluster centers with the existing cluster centers in the current cluster codebook, and update the status flags corresponding to each cluster center to obtain the target cluster codebook.

[0248] The current user representation vector is a continuous vector obtained by numerically representing the interest state of the target user, and it is used as input data in the clustering codebook update process. Specifically, the current user representation vector can include a fixed-dimensional dense vector output by deep neural network encoding the multi-dimensional behavioral data of the target user within a preset time window. The behavioral data can include aggregated features of various behavioral types such as browsing, clicking, adding to cart, favoriting, and placing orders. The current user representation vector can cover the representation vector set of all active users on the platform or a subset of users who meet preset activity conditions. The current user representation vector can serve as the input object for clustering codebook coverage evaluation and incremental updates, and its distribution characteristics directly determine whether the codebook needs to be expanded and the direction of expansion.

[0249] The current clustering codebook is an encoded dictionary constructed by performing multi-level clustering on historical user representation vectors. It maps continuous user representation vectors to discrete semantic identifiers. Specifically, the current clustering codebook may include multiple cluster centers divided according to clustering levels and a state identifier corresponding to each cluster center. The multiple cluster centers are organized hierarchically. The first-level cluster centers perform overall clustering of the original user representation vectors. Subsequent levels of cluster centers progressively refine the clustering residuals from the previous level. The combination of the cluster center numbers at each level constitutes the user's semantic identifier. The current clustering codebook can be constructed using a residual quantization clustering algorithm, determining the position of each cluster center by minimizing the quantization error layer by layer.

[0250] Clustering hierarchy is a multi-layered, progressively refined clustering organization structure in the current clustering codebook, with each layer corresponding to a different granularity of user representation space partitioning. Specifically, the clustering hierarchy can include a progressive structure from the first layer to several layers. The first layer performs coarse-grained overall clustering on the original user representation vectors. The second layer further clusters the residual vectors generated by the first layer's clustering. Subsequent layers sequentially perform finer-grained clustering on the residuals from the previous layer. The cluster center numbers at each layer are combined in hierarchical order to form the user's complete semantic identifier. The clustering hierarchy enables the semantic identifier to have multi-granularity representation capabilities from coarse to fine, with cluster centers at different levels capturing different levels of abstraction of user interests.

[0251] Cluster centers are the centroid vectors of each cluster in the current clustering codebook, representing the central tendency and typical characteristics of user representation vectors within that cluster in the vector space. Specifically, cluster centers can include fixed-dimensional coordinate vectors determined through iterative optimization during the construction of the clustering codebook. Each cluster center corresponds to an encoding slot. The cluster center number to which a user representation vector belongs is determined by calculating its distance from the cluster centers at each layer. The set of cluster centers constitutes the core data structure of the codebook. Cluster centers serve as a reference benchmark for the discretization encoding of user representation vectors, and their positional accuracy directly affects the representation quality of semantic identifiers.

[0252] Status identifiers are status markers for each coding slot in the current clustering codebook, used to characterize the current availability and lifecycle stage of the corresponding cluster center. Specifically, status identifiers can include three types: active, inactive, and reserved. An active status indicates that the corresponding cluster center currently has a user affiliation and its characterization quality meets requirements. An inactive status indicates that the corresponding cluster center is marked as unavailable due to failing quality assessment. A reserved status indicates that the corresponding coding slot is reserved for future expansion but has not yet been activated. Status identifiers can be used to manage the availability of each coding slot during codebook updates. When a new cluster center is added to the codebook, its status identifier is set to active; when an existing cluster center fails quality assessment, its status identifier is updated to inactive.

[0253] The centroid vector is a fixed-dimensional coordinate vector corresponding to a cluster center, used to calculate the distance between the user representation vector and each cluster center. Specifically, the centroid vector can include the centroid coordinates determined through iterative optimization during the construction or updating of the cluster codebook. Each cluster center has a unique corresponding centroid vector, and the dimension of the centroid vector is consistent with the dimension of the user representation vector. The distance between the two can be calculated using Euclidean distance, cosine distance, or other metrics. The centroid vector can serve as the basis for coverage evaluation, determining whether a user is sufficiently covered by existing cluster centers by comparing the distances between the current user representation vector and each centroid vector.

[0254] Uncovered user representation vectors are a subset of current user representation vectors that are not fully covered by existing cluster centers. Specifically, these are user representation vectors whose distances to the center vectors of all existing cluster centers do not meet the coverage condition. Specifically, uncovered user representation vectors can include those whose distance to the center vector of the nearest cluster center exceeds a preset coverage distance threshold, or those falling into sparsely distributed regions of existing cluster centers. Their common characteristic is that the existing codebook cannot provide discretization encodings with sufficient accuracy. Uncovered user representation vectors can serve as the basis for determining new cluster centers; their distribution characteristics determine the location and number of new cluster centers.

[0255] The newly added cluster centers are new centroid vectors determined based on the uncovered user representation vectors, used to supplement the coverage capability of the current cluster codebook. Specifically, the newly added cluster centers can include one or more centroid vectors obtained after clustering the uncovered user representation vectors. Each newly added cluster center corresponds to a dense region in the distribution space of the uncovered user representation vectors, and its location is determined by the distribution characteristics of the uncovered user representation vectors. The newly added cluster centers can expand the coverage of the codebook without modifying the positions of existing cluster centers, providing discretized coding with sufficient accuracy for user groups that were previously not fully covered.

[0256] The target clustering codebook is an updated codebook obtained by merging newly added cluster centers with multiple existing cluster centers in the current clustering codebook. It serves as the encoding benchmark for subsequent semantic label determination. Specifically, the target clustering codebook can include a complete set of all existing cluster centers and newly added cluster centers. Each cluster center corresponds to an updated status label, with the status labels of newly added cluster centers set to active. The status labels of existing cluster centers remain unchanged or are updated based on quality assessment results. The target clustering codebook can be used as an updated encoding dictionary to assign semantic labels to the current user's representation vector. Users determine their assigned cluster center number combination by calculating the distance between their representation vector and the cluster centers at each layer in the target clustering codebook.

[0257] In practical applications, obtaining the current user representation vector and the current cluster codebook can be achieved through various methods.

[0258] One alternative approach is to batch read the user representation vectors of all active users within the current time window from the platform's user feature storage system, and simultaneously load the current version of the clustering codebook data structure and the status identifier information of each cluster center from the codebook management service, using both as input data for the codebook update task. This method obtains complete data through centralized batch loading, making it suitable for offline, timed codebook update scenarios, offering good data consistency and a simple implementation.

[0259] Another alternative approach is to obtain the current user representation vector through an incremental data synchronization mechanism. This involves only retrieving user representation vectors that have been added or changed since the last codebook update, while simultaneously reading a snapshot version of the current clustering codebook from the distributed codebook storage. This reduces data transfer volume and storage access pressure. This method is suitable for scenarios with a large user base and frequent codebook updates, effectively reducing the data loading overhead for each update task.

[0260] In addition, a streaming access method can be used to continuously receive user representation vectors calculated in real time. After accumulating to a preset batch size, the codebook update process is triggered, and the current clustering codebook is read from the memory cache.

[0261] Given the current user representation vector and the current cluster codebook, the uncovered user representation vector can be determined from the current user representation vector based on the distance between the current user representation vector and the center vectors of multiple cluster centers.

[0262] One alternative approach is to calculate the Euclidean distance between each user representation vector in the current user representation vector and the center vectors of all cluster centers in the current clustering codebook, and then identify user representation vectors whose minimum distance exceeds a preset coverage distance threshold as uncovered user representation vectors. By using the distance threshold as the sole criterion, the logic is clear and the judgment standard is uniform, making it suitable for codebooks with a moderate number of cluster centers.

[0263] Another alternative approach is to perform outlier detection on the current user representation vectors. Outlier scores are calculated based on the distance distribution between each user representation vector and the center vector of its nearest cluster. User representation vectors with outlier scores exceeding a preset outlier threshold are identified as uncovered user representation vectors. This method identifies anomalous vectors that deviate from existing cluster centers from a statistical distribution perspective, and its dependence on the coverage distance threshold is relatively low.

[0264] Another alternative approach is to estimate the density of the overall distribution of current user representation vectors based on kernel density estimation. User representation vectors located in low-density regions and far from the nearest cluster centers are identified as uncovered user representation vectors, taking into account both distance and distribution density criteria. This method can identify user groups in sparsely distributed areas of existing cluster centers and is suitable for scenarios with uneven distribution of user interests.

[0265] In addition, a comprehensive judgment can be made by combining cluster coverage and residual distribution statistics. When the coverage index is lower than the preset standard, the user representation vector with a large residual is identified as the uncovered user representation vector.

[0266] Once the uncovered user representation vectors are determined, new cluster centers can be identified based on these vectors.

[0267] One alternative approach is to perform clustering on the uncovered user representation vectors, using the centroid vectors of each cluster as the new cluster centers. The number of clusters is determined based on the size and dispersion of the uncovered user representation vectors. This method directly extracts the centroids of dense regions from the uncovered vectors as the new cluster centers, and the locations of these new cluster centers accurately reflect the distribution characteristics of the uncovered user group.

[0268] Another alternative approach is to select the vector with the highest distribution density among the uncovered user representation vectors as the initial centroid. The centroid position is then iteratively adjusted until convergence. The converged centroid vector is then identified as the new cluster center. This process is repeated until the uncovered user representation vectors are fully covered. This method determines new cluster centers iteratively, re-evaluating the distribution of the remaining uncovered vectors after each new cluster center is added, resulting in higher accuracy in locating the new cluster centers.

[0269] Another alternative approach is to determine the principal component analysis results of the uncovered user representation vectors to identify the main direction of the distribution. Then, sampling is performed along the principal component direction at preset intervals to determine the initial positions of the new cluster centers. These initial positions are then adjusted locally to determine the final locations of the new cluster centers. This method utilizes the principal direction information of the distribution to guide the layout of the new cluster centers and is suitable for scenarios where the uncovered user representation vectors exhibit a linear or ellipsoidal distribution.

[0270] In addition, the vector that is furthest from the existing cluster center among the uncovered user representation vectors can be directly identified as the new cluster center.

[0271] Once a new cluster center is identified, it can be merged with multiple existing cluster centers in the current cluster codebook, and the status flags corresponding to each cluster center can be updated to obtain the target cluster codebook.

[0272] One alternative approach is to append new cluster centers to the current cluster center list in the clustering codebook, assign a new coding slot to each new cluster center and set its status to active, while keeping the positions and statuses of existing cluster centers unchanged. The merged complete set of cluster centers and its status information are then encapsulated into the target clustering codebook for persistent storage. This method performs the merging process append-only, leaving the existing coding system completely unaffected, achieving simplicity and backward compatibility.

[0273] Another alternative approach is to perform a quality assessment on existing cluster centers while merging new cluster centers. Existing cluster centers that fail the quality assessment are updated to an invalid state, while the newly added cluster centers are set to an active state. The target cluster codebook is then generated based on all updated status indicators. This method expands coverage while maintaining codebook quality, and invalid slots can be reused in subsequent updates.

[0274] Another alternative approach is to perform a global consistency check on the target cluster codebook after the merging process. This checks whether there is any overlap between the newly added cluster centers and existing cluster centers. If overlap exists, the positions of the newly added cluster centers are adjusted or the overlapping cluster centers are merged. Once the check passes, the target cluster codebook is released as the new, effective version. This method ensures that the coverage areas of each cluster center in the target cluster codebook do not conflict, avoiding ambiguity in the encoding of user representation vectors.

[0275] In addition, a versioned management approach can be used to record the change log of the target cluster codebook relative to the current cluster codebook, supporting rollback to the previous version in case of update anomalies.

[0276] In the embodiments of this specification, the uncovered user representation vector is determined based on the distance between the current user representation vector and the center vector of the cluster center, thus providing a clear quantitative criterion for identifying codebook coverage gaps; new cluster centers are determined based on the uncovered user representation vectors, ensuring that the codebook expansion direction is driven by actual coverage gaps rather than blindly increasing the number of codes; by merging new cluster centers with existing cluster centers and updating the status identifier, the codebook maintains the integrity and stability of the existing coding system while expanding its coverage capability; and by obtaining the target cluster codebook, an updated coding benchmark with satisfactory coverage accuracy is provided for the subsequent determination of semantic identifiers.

[0277] Corresponding to the above method embodiments, this specification also provides an embodiment of a community content recommendation method, specifically, see [link to embodiment]. Figure 3 , Figure 3 A flowchart illustrating a community content recommendation method provided in one embodiment of this specification is shown. Figure 3 As shown, the community's content recommendation method includes the following steps.

[0278] Step 302: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; Step 304: Input the historical semantic identifier sequence into the pre-trained prediction model and output the semantic identifier probability distribution in the future time period; Step 306: Determine the predicted semantic identifier based on the semantic identifier probability distribution, and determine the target user's interest migration direction based on the semantic association between the predicted semantic identifier and semantic identifiers in multiple historical time periods; Step 308: Recommend target community content that corresponds to the direction of interest migration to the target users.

[0279] In community content recommendation scenarios, semantic identifiers can be multi-layered discrete identifiers representing the target user's community content interaction preferences. Each semantic identifier corresponds to a user cluster with clear business semantics within the community content interest space. Specifically, semantic identifiers can be encoded and quantified based on the target user's content interaction behavior in the community scenario. Community content interaction behavior can include various community interaction behaviors such as browsing notes, liking posts, commenting, collecting and sharing, and following authors. User clusters corresponding to different semantic identifiers have distinguishable interest characteristics in the dimension of community content preferences. For example, users in one cluster tend to browse travel notes, while users in another cluster tend to browse food review posts. Semantic identifiers can reflect the target user's interest position in the community content ecosystem, and changes in them correspond to substantial shifts in community content interaction preferences.

[0280] In the context of community content recommendation, a historical semantic tag sequence can be an ordered record of a target user's community content interaction preferences across multiple consecutive historical time periods, arranged chronologically. Specifically, each element in the historical semantic tag sequence corresponds to the user cluster to which the target user belongs based on their community content interaction behavior within the corresponding historical time period. The sequence as a whole reflects the evolutionary trajectory of the target user's community content interests, such as a gradual migration from a travel content interaction cluster to a food content interaction cluster. The historical semantic tag sequence can serve as input to a community content recommendation prediction model, providing the model with complete observational information on how the target user's community content preferences change over time.

[0281] In community content recommendation scenarios, predictive semantic tags can be identified from the probability distribution of semantic tags, representing the community content interest cluster to which a target user is most likely to belong in the future. Specifically, predictive semantic tags can reflect the direction of a target user's upcoming shift in community content interaction preferences. For example, if it is predicted that a target user will migrate from the current travel content interaction cluster to the outdoor sports content interaction cluster, then the cluster corresponding to the predictive semantic tag represents the interaction group of outdoor sports-related community content in terms of business semantics. Predictive semantic tags can serve as the core basis for adjusting community content recommendation strategies, enabling community content distribution to complete forward-looking adjustments on the supply side before the actual shift in user interests.

[0282] In community content recommendation scenarios, the direction of interest migration can be seen as the changing trend and direction of a target user's community content interaction preferences from the current historical time period to the predicted future time period. Specifically, the direction of interest migration can include cross-cluster migration of a target user from the current community content interest cluster to another community content interest cluster, as well as the stable self-loop state of a target user within the current community content interest cluster. For example, migration from a beauty and skincare note interaction cluster to a home decoration note interaction cluster, or continuous stability within a sports and fitness post interaction cluster. The direction of interest migration can serve as a direct basis for adjusting community content recommendation categories, enabling the recommendation system to adjust the community content supply structure in a targeted manner based on the predicted direction of interest changes.

[0283] Targeted recommended community content refers to community-based content items selected and pushed to target users based on their interest migration direction. Specifically, targeted recommended community content can include notes, posts, topic discussions, user-generated images and text, short video updates, and other content formats centered on community interaction. This differs from product detail pages and product cards, which are directly aimed at transaction conversion; it emphasizes interest expression, experience sharing, community interaction, and content interaction. Targeted recommended community content is determined by filtering and sorting content categories from the community content pool based on the interest migration direction. Filtering dimensions can include community-specific attributes such as content topic category, content style tags, and author's domain affiliation.

[0284] In practical applications, recommending target community content that aligns with the direction of interest migration to target users can be achieved through various methods.

[0285] One alternative approach is to determine the target community content category based on the direction of interest migration. Candidate notes and posts belonging to the target category are then selected from the community content pool and ranked comprehensively based on content quality score, posting timeliness, interaction popularity, and author credibility. The top-ranked community content items are then designated as target recommended community content and pushed to the target users. This method filters and ranks content at the granular level of community content category, ensuring a clear correspondence between the recommendation results and the direction of interest migration across the community content themes. This facilitates understanding and review of the recommendation logic by operations personnel.

[0286] Another alternative approach is to introduce interest migration direction as an additional ranking signal into the candidate scoring stage of the community content recommendation ranking model. Candidate community content that matches the content category indicated by the migration direction is given a positive ranking weight, while candidate community content that does not conform to the migration direction is given a negative ranking penalty. The target recommended community content is then determined based on the adjusted ranking score. This approach can integrate migration direction guidance information into the existing community content recommendation architecture with minimal modification cost, maintaining the overall stability of the recommendation system while achieving directional guidance.

[0287] Another option is to build a personalized community content candidate pool for target users based on their interest migration direction. The community content in the candidate pool is prioritized according to its match with the migration direction. Each time a target user initiates a community content browsing request, recommended community content is first selected from the personalized candidate pool. If the candidate pool is insufficient, it is supplemented from the general community content pool using a standard strategy. This approach ensures that the recommended community content highly aligns with the predicted changes in community content interests, enabling a rapid response and push of matching content when a user's community interests shift.

[0288] In addition, the direction of interest migration can be encoded as contextual feature information in community content recommendation requests, and input into the recommendation ranking model together with user community behavior profile features, community content attribute features, and social relationship features. The model can then automatically learn the influence weight of migration direction on community content preferences during feature interaction.

[0289] In the embodiments of this specification, by applying a semantic identifier system to the community content recommendation scenario, user community content interaction preferences are fully characterized in the form of discrete symbols, providing standardized input for community content interest prediction; by applying the direction of interest migration to the screening and sorting of community content categories, community content distribution can be proactively adjusted based on the predicted future state of community interests; by limiting the recommended objects to community-based content items such as notes and posts, the recommendation results are adapted to the interactive characteristics of the community interaction scenario; and by driving the adjustment of the community content supply structure with the direction of interest migration, the ability of community content recommendation is improved from passive response to proactive prediction.

[0290] Corresponding to the above method embodiments, this specification also provides an embodiment of a product content recommendation method, specifically, see [link to embodiment]. Figure 4 , Figure 4 A flowchart illustrating a product content recommendation method according to one embodiment of this specification is shown. Figure 4 As shown, the method for recommending product content includes the following steps.

[0291] Step 402: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; Step 404: Input the historical semantic identifier sequence into the pre-trained prediction model and output the semantic identifier probability distribution in the future time period; Step 406: Determine the predicted semantic identifier based on the semantic identifier probability distribution, and determine the target user's interest migration direction based on the semantic association between the predicted semantic identifier and semantic identifiers in multiple historical time periods; Step 408: Recommend target product content that corresponds to the direction of interest migration to the target user.

[0292] In product content recommendation scenarios, semantic identifiers can be multi-layered discrete identifiers representing the target user's product consumption preferences and purchasing power level. Each semantic identifier corresponds to a user cluster with clear business semantics within the product interest space. Specifically, semantic identifiers can be encoded and quantified based on the target user's product consumption behavior in e-commerce scenarios. Product consumption behavior can include various transaction link behaviors such as browsing, adding to cart, favorites, placing orders, and reviews. User clusters corresponding to different semantic identifiers have distinguishable value characteristics in terms of product category preferences and purchasing power. For example, one cluster of users may focus on consuming digital electronics with a higher average order value, while another cluster may focus on consuming daily necessities and be primarily driven by promotional sensitivity. Semantic identifiers can reflect the target user's consumption level and category preference position within the e-commerce ecosystem, and changes in these reflect substantial shifts in product consumption preferences.

[0293] In product content recommendation scenarios, historical semantic tag sequences can be ordered records of a target user's product consumption preferences across multiple consecutive historical time periods, arranged chronologically. Specifically, each element in the historical semantic tag sequence corresponds to the user cluster to which the target user belongs based on their product consumption behavior within the corresponding historical time period. The sequence as a whole reflects the evolutionary trajectory of the target user's product category preferences and consumption levels, such as the gradual migration from the digital electronics consumption cluster to the home furnishing and decoration consumption cluster. Historical semantic tag sequences can serve as input to product content recommendation prediction models, providing the models with complete observational information on the changes in the target user's product consumption preferences over time.

[0294] In product content recommendation scenarios, predictive semantic tags can be identified from the semantic tag probability distribution, representing the cluster of product consumption interests to which a target user is most likely to belong in the future. Specifically, predictive semantic tags can reflect the direction of a target user's impending shift in product consumption preferences. For example, if it is predicted that a target user will migrate from the current digital electronics consumption cluster to the sports and outdoor consumption cluster, then the cluster corresponding to the predictive semantic tag represents the consumer group for sports and outdoor products in terms of business semantics. Predictive semantic tags can serve as the core basis for adjusting product content recommendation strategies, enabling proactive adjustments to the supply side of product content distribution before the actual shift in user consumption preferences.

[0295] In product content recommendation scenarios, the direction of interest migration can be seen as the changing trend and direction of a target user's product consumption preferences from the current historical time period to the predicted future time period. Specifically, the direction of interest migration can include cross-cluster migration of a target user from the current product consumption cluster to another product consumption cluster, as well as a stable self-loop state within the current product consumption cluster, such as migration from the maternal and infant product consumption cluster to the children's education consumption cluster, or continuous stability within the sports and fitness product consumption cluster. The direction of interest migration can serve as a direct basis for adjusting product content recommendation categories, enabling the recommendation system to adjust the product content supply structure in a targeted manner based on the predicted direction of changes in consumption preferences.

[0296] Targeted product recommendations are product content items selected and pushed to target users based on their interest migration direction. Specifically, targeted product recommendations can include product detail pages, product cards, short product videos, live-streaming sales links, and product review images and text—content formats whose core purpose is product display and transaction conversion. This differs from content types like community notes and topic discussions, which focus on interest-based interaction; it emphasizes product attribute display, purchase decision assistance, and transaction path guidance. Targeted product recommendations are determined by filtering and sorting product categories from a product content pool based on the interest migration direction. Filtering dimensions can include product category, price range, brand level, and promotional status—all product-specific attributes. Furthermore, the recommendation of targeted product content aims to guide target users to complete the conversion from interest to consumption in e-commerce scenarios. By pre-supplying product content consistent with predicted consumption preferences, it shortens the conversion path from initial interest to purchase decision, shifting product content distribution from passive recommendation based on historical purchase behavior to proactive supply based on predictions of future consumption preferences.

[0297] In practical applications, recommending targeted product content that aligns with the direction of interest migration to target users can be achieved through various methods.

[0298] One alternative approach is to determine the target product category based on the direction of interest migration, filter candidate products belonging to the target category from the product content pool, and then rank them comprehensively based on product conversion rate, user review score, inventory status, and price competitiveness. The top-ranked products are then selected as target recommended products and pushed to the target users. This method filters and ranks products at the product category level, and the recommendation results have a clear correspondence with the direction of interest migration in terms of product consumption, making it easier for operations personnel to understand and review the recommendation logic.

[0299] Another alternative approach is to introduce interest migration direction as an additional ranking signal into the candidate scoring stage of the product recommendation ranking model. Candidate products that match the product category indicated by the migration direction are given a positive ranking weight, while candidate products that do not conform to the migration direction are given a negative ranking penalty. The target recommended product content is then determined based on the adjusted ranking score. This approach can integrate migration direction guidance information into the existing product recommendation architecture with minimal modification cost, maintaining the overall stability of the recommendation system while achieving category-oriented guidance.

[0300] Another option is to build a personalized product candidate pool for target users based on their interest migration direction. Products in the candidate pool are prioritized according to their match with the migration direction. Each time a target user initiates a product browsing request, the recommended products are first selected from the personalized candidate pool. If the candidate pool is insufficient, products are supplemented from the general product pool using a standard strategy. This approach ensures that the recommended product content closely matches the predicted changes in consumer preferences, allowing for a rapid response and push of matching products when user consumption interests shift.

[0301] In addition, the direction of interest transfer can be encoded as contextual feature information in the product recommendation request, and input into the recommendation ranking model together with user purchasing power profile features, product attribute features, and promotional activity features. The model can then automatically learn the influence weight of the transfer direction on product preferences during feature interaction.

[0302] In the embodiments of this specification, by applying a semantic identifier system to the product content recommendation scenario, user product consumption preferences are fully characterized in the form of discrete symbols, providing standardized input for product interest prediction; by applying the direction of interest migration to the screening and sorting of product categories, product content distribution can be proactively adjusted based on the predicted future consumption preference state; by limiting the recommendation objects to product detail pages, product cards, and other product-related content items, the recommendation results are adapted to the consumption characteristics of e-commerce transaction scenarios; by driving the adjustment of the product content supply structure with the direction of interest migration, the ability of product content recommendation has been improved from a passive response based on historical behavior to an active supply based on the prediction of future preferences.

[0303] Corresponding to the above method embodiments, this specification also provides embodiments of a content recommendation device, specifically, see [link to relevant documentation]. Figure 5 , Figure 5 A schematic diagram of a content recommendation device according to one embodiment of this specification is shown. Figure 5 As shown, the device includes: The acquisition module 502 is configured to acquire the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; The prediction module 504 is configured to input historical semantic identifier sequences into a pre-trained prediction model and output the semantic identifier probability distribution within a future time period. The determination module 506 is configured to determine the predicted semantic identifier based on the semantic identifier probability distribution, and determine the target user's interest migration direction based on the semantic association between the predicted semantic identifier and semantic identifiers in multiple historical time periods. Recommendation module 508 is configured to recommend target content to target users that corresponds to their interest migration direction.

[0304] The acquisition module is the functional unit in the content recommendation device that undertakes data collection and sequence organization. Specifically, the acquisition module may include a processor, memory, data interface, and corresponding control circuits. It can also be implemented as a software program, firmware instructions, or a combination of hardware and software running on a server or terminal device, and can communicate with data sources such as databases, log systems, and content platform backends. The acquisition module can read the semantic tags corresponding to the content that the target user has encountered or interacted with in multiple historical time periods from the target user's historical behavior records, and organize these semantic tags into a historical semantic tag sequence according to chronological order.

[0305] The prediction module is a functional unit in the content recommendation device that undertakes model inference and probability output. Specifically, the prediction module may include a neural network inference engine, a model parameter storage unit, a tensor computation unit, and corresponding hardware acceleration circuitry. It can also be implemented as a model service program deployed on a server, embedded firmware, or a hardware-software co-operation model. It can interact with the acquisition module to receive historical semantic tag sequences. The prediction module can use the historical semantic tag sequences output by the acquisition module as input, load a pre-trained prediction model for forward inference calculation, and output the probability distribution of each candidate semantic tag being hit within a future time period.

[0306] The determination module is a functional unit in the content recommendation device that undertakes semantic parsing and direction determination. Specifically, the determination module may include a comparator, a semantic distance calculation unit, an association analysis unit, and corresponding logic control circuits. It can also be implemented as an algorithm program, firmware instructions, or a combination of hardware and software running on a server, and can interact with the prediction module to receive the semantic identifier probability distribution. The determination module can select a predicted semantic identifier based on the probability value of each candidate semantic identifier in the probability distribution, and compare the predicted semantic identifier with semantic identifiers in multiple historical time periods to determine the direction of the target user's interest migration.

[0307] The recommendation module is a functional unit in a content recommendation device that is responsible for content matching and result output. Specifically, the recommendation module may include a content retrieval unit, a sorting unit, a display control unit, and corresponding communication interface circuits. It can also be implemented as a recommendation service program, firmware instructions, or a combination of hardware and software running on a server or terminal device. It can interact with the determining module to receive interest migration directions and communicate with the content library or content distribution system. The recommendation module can retrieve target recommended content matching the interest migration direction from the candidate content pool and push it to the target user in a list, card, information stream, or other appropriate presentation format.

[0308] The content recommendation device provided in this specification, through the coordinated operation of an acquisition module, a prediction module, a determination module, and a recommendation module, achieves automated identification of the target user's interest migration direction and accurate push of corresponding content. This avoids the limitations of traditional recommendation methods that rely solely on static historical preferences while ignoring dynamic changes in interests. By using semantic identifier sequences as input, the acquisition module enables the device to model user behavior at a semantic abstraction level, reducing the interference of specific content differences on interest characterization and improving the device's applicability and generalization ability across different content formats. The prediction module outputs the semantic identifier probability distribution for future time periods, which the determination module then uses to determine the interest migration direction. This provides the device's recommendation decisions with quantitative evidence and interpretable semantic direction, enhancing the objectivity and controllability of the recommendation process. The recommendation module pushes target recommended content corresponding to the interest migration direction to the target user, ensuring that the recommendation results match the user's current real needs, improving the user's content acquisition experience, and contributing to improving the overall distribution efficiency and user stickiness of the content distribution platform.

[0309] The above is an illustrative scheme of a content recommendation device according to this embodiment. It should be noted that the technical solution of this content recommendation device and the technical solution of the content recommendation method described above belong to the same concept. For details not described in detail in the technical solution of the content recommendation device, please refer to the description of the technical solution of the content recommendation method described above.

[0310] Figure 6 A structural block diagram of a computing device 600 according to one embodiment of this specification is shown. The components of the computing device 600 include, but are not limited to, a memory 610 and a processor 620. The processor 620 is connected to the memory 610 via a bus 630, and a database 650 is used to store data.

[0311] The computing device 600 also includes an access device 640, which enables the computing device 600 to communicate via one or more networks 660. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 640 may include one or more of any type of wired or wireless network interface (e.g., a network interface controller (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0312] In one embodiment of this specification, the above-described components of the computing device 600 and Figure 6 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 6 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0313] The computing device 600 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 600 can also be a mobile or stationary server.

[0314] The processor 620 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above method.

[0315] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above method belong to the same concept, and all details not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the above method.

[0316] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described method.

[0317] The above is an illustrative embodiment of a computer-readable storage medium. It should be noted that the technical solution of this storage medium and the technical solution of the method described above belong to the same concept; details not described in detail in the technical solution of the storage medium can be found in the description of the technical solution of the method described above.

[0318] An embodiment of this specification also provides a computer program product, including computer-executable instructions that, when executed by a processor, implement the steps of the above-described method.

[0319] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the above method belong to the same concept, and all details not described in detail in the technical solution of the computer program product can be referred to in the description of the technical solution of the above method.

[0320] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0321] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0322] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0323] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0324] The preferred embodiments disclosed above are merely illustrative of this specification. Optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A content recommendation method, characterized in that, include: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; The historical semantic identifier sequence is input into a pre-trained prediction model, which outputs the semantic identifier probability distribution within a future time period. Based on the semantic identifier probability distribution, a predicted semantic identifier is determined, and based on the semantic association between the predicted semantic identifier and the semantic identifiers in the multiple historical time periods, the interest migration direction of the target user is determined. Recommended content that corresponds to the direction of interest migration to the target user.

2. The method according to claim 1, characterized in that, The acquisition of the target user's historical semantic identifier sequence includes: According to a preset snapshot interval, the semantic identifiers of the target user are periodically sampled to obtain semantic identifier snapshots corresponding to each time period within the multiple historical time periods. Arrange the semantic identifier snapshots corresponding to each time period in chronological order to obtain the historical semantic identifier sequence.

3. The method according to claim 1, characterized in that, The step of inputting the historical semantic identifier sequence into a pre-trained prediction model and outputting the semantic identifier probability distribution within a future time period includes: Based on the transition relationships between semantic identifiers in the historical semantic identifier sequence, the prediction model is used to perform sequence modeling and output the probability value of each candidate semantic identifier in the future time period. The probability distribution of the semantic identifier is determined based on the probability value of each candidate semantic identifier.

4. The method according to claim 3, characterized in that, The pre-trained prediction model includes at least one of the following: a lightweight baseline model, a recurrent neural network model, a self-attention regression model, and a graph neural network model. The step of performing sequence modeling based on the transition relationships between semantic identifiers in the historical semantic identifier sequence, and outputting the probability value of each candidate semantic identifier in the future time period, includes: When the prediction model is the lightweight baseline model, the state transition probability is determined based on the jump frequency between adjacent semantic identifiers in the historical semantic identifier sequence, and the probability value of each candidate semantic identifier in the future time period is output based on the state transition probability. When the prediction model is the recurrent neural network model, each semantic identifier in the historical semantic identifier sequence is mapped to a continuous vector representation. The continuous vector representation is sequence encoded by the recurrent neural network model to obtain a hidden state representation. Based on the hidden state representation, the probability value of each candidate semantic identifier in the future time period is output. When the prediction model is the self-attention regression model, each semantic identifier in the historical semantic identifier sequence is mapped to a continuous vector representation. The continuous vector representation is then self-attention encoded by the self-attention autoregression model to obtain a sequence context representation. Based on the sequence context representation, the probability value of each candidate semantic identifier in the future time period is output. When the prediction model is the graph neural network model, a directed graph is constructed based on the historical jump relationship between user clusters corresponding to each semantic identifier. The directed graph is modeled by the graph neural network model to output the probability value of each candidate semantic identifier in the future time period.

5. The method according to claim 3, characterized in that, The future time period includes multiple time periods; The historical semantic identifier sequence is input into a pre-trained prediction model, and the output semantic identifier probability distribution for future time periods includes: The historical semantic identifier sequence is input into a pre-trained prediction model, which outputs the probability distribution of the next semantic identifier in the next future time period. The next predicted semantic identifier is determined based on the probability distribution of the next semantic identifier, and the next predicted semantic identifier is appended to the historical semantic identifier sequence to obtain the updated historical semantic identifier sequence. The updated historical semantic identifier sequence is input into the prediction model, and the probability distribution of semantic identifiers in the multiple future time periods is iteratively output.

6. The method according to claim 1, characterized in that, The step of determining the predicted semantic identifier based on the semantic identifier probability distribution includes: Based on the confidence level of each semantic identifier in the semantic identifier probability distribution and the preset confidence threshold, a prediction candidate set is determined from each semantic identifier; Based on the predicted candidate set, the predicted semantic identifier is determined.

7. The method according to any one of claims 1-6, characterized in that, Determining the target user's interest migration direction based on the semantic association between the predicted semantic identifier and semantic identifiers within the multiple historical time periods includes: Based on the posterior behavioral data of the target users, determine the interest semantic features of the target user clusters corresponding to the predicted semantic identifiers; Based on the differences between the interest semantic features and the interest semantic features of user clusters corresponding to semantic identifiers in the multiple historical time periods, the direction of interest migration is determined.

8. The method according to any one of claims 1-6, characterized in that, The step of recommending target content to the target user that corresponds to the direction of interest migration includes: Based on the target users' interest migration direction, a target content recommendation strategy is determined; Based on the target content recommendation strategy, target recommended content corresponding to the direction of interest migration is recommended to the target user.

9. The method according to claim 8, characterized in that, The directions of interest migration include upward migration, downward migration, and stable / unchanged migration. The step of determining the target content recommendation strategy based on the target user's interest migration direction includes: When the direction of interest migration is upward migration, the target content recommendation strategy is determined to be a conversion acceleration strategy; When the direction of interest migration is downward migration, the target content recommendation strategy is determined to be a churn retention strategy. If the direction of interest migration remains stable, the target content recommendation strategy is determined to be a supply maintenance strategy.

10. The method according to claim 9, characterized in that, The step of recommending target content to the target user based on the target content recommendation strategy, which corresponds to the direction of interest migration, includes: When the target content recommendation strategy is the conversion acceleration strategy, the target user cluster corresponding to the predicted semantic identifier is determined; Increase the recommendation weight of recommended content types that have a positive correlation with the target user cluster; Based on the improved recommendation weight, target recommendation content corresponding to the direction of interest migration is recommended to the target user.

11. The method according to claim 9, characterized in that, The step of recommending target content to the target user based on the target content recommendation strategy, which corresponds to the direction of interest migration, includes: When the target content recommendation strategy is the churn retention strategy, determine the current user cluster to which the target user currently belongs; Maintain the recommendation weight of content types that have a positive correlation with the current user cluster, and reduce the recommendation weight of content types that have a negative correlation with the current user cluster; Based on the adjusted recommendation weights, target recommendation content corresponding to the direction of interest migration is recommended to the target user.

12. A community content recommendation method, characterized in that, include: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; The historical semantic identifier sequence is input into a pre-trained prediction model, which outputs the semantic identifier probability distribution within a future time period. Based on the semantic identifier probability distribution, a predicted semantic identifier is determined, and based on the semantic association between the predicted semantic identifier and the semantic identifiers in the multiple historical time periods, the interest migration direction of the target user is determined. Recommend target community content that corresponds to the direction of interest migration to the target user.

13. A method for recommending product content, characterized in that, include: Obtain the historical semantic identifier sequence of the target user, wherein the historical semantic identifier sequence includes the semantic identifiers of the target user in multiple historical time periods; The historical semantic identifier sequence is input into a pre-trained prediction model, which outputs the semantic identifier probability distribution within a future time period. Based on the semantic identifier probability distribution, a predicted semantic identifier is determined, and based on the semantic association between the predicted semantic identifier and the semantic identifiers in the multiple historical time periods, the interest migration direction of the target user is determined. Recommended product content that corresponds to the direction of interest migration to the target user.

14. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 13.

15. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 13.

16. A computer program product, characterized in that, It includes computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 13.

Images