A precise push method and system based on user portrait

Abstract

The present invention provides a user portrait-based precision push method and system, which relates to the field of data processing technology. The method includes: obtaining category labels for each data content in the data library; obtaining user behavior data on the data content corresponding to each category label; combining the behavior data and a hidden Markov model to generate state transition probabilities and user preference labels; generating a user portrait with a push time period; obtaining the category labels of the data content currently accessed by the user; calculating the similarity values ​​between each category label and the category label of the data content currently accessed by the user; combining the user portrait and the similarity values ​​to generate the push data content at the next moment; recording the length of time the user stays under the push data content; setting a correction factor to correct the state transition probability corresponding to the category label of the data content currently accessed by the user; using the corrected state transition probability as the new state transition probability and repeatedly executing the push. This improves push accuracy and increases user stickiness.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a user portrait-based precise push method and system. Background Art

[0002] A user portrait is a detailed description built based on the user's behavioral data. It is used to characterize the user's characteristics and interests, enabling companies to understand and predict user needs and behaviors. By analyzing the collected data, user portraits help improve the accuracy and efficiency of personalized services.

[0003] In an age of information overload, users often struggle to effectively filter content that is truly valuable to them. Precision push notifications leverage user profiles to identify users' specific needs and preferences, providing customized information and services. For example, e-commerce platforms can recommend products based on their purchase history and browsing habits, increasing sales opportunities, while media services can push news or videos that interest users, boosting engagement and satisfaction. Furthermore, precision push notifications help companies optimize advertising effectiveness by displaying relevant ads to specific target groups, increasing conversion rates and reducing resource waste. In short, precision push notifications not only strengthen user engagement and enhance user experience, but also bring economic benefits and increased brand loyalty to businesses, significantly improving user experience and business efficiency.

[0004] However, although most existing data push methods also adopt a push method based on user portraits, they are often only recommendations based on the same category tags of historical data. To be precise, it is a retrieval-based push method that does not fully capture user preferences. When the same category tags do not appear in the data in the database, effective data push cannot be performed. This method relies heavily on the category tags of the data and is prone to blind push, resulting in reduced user stickiness and affecting business operations. Summary of the Invention

[0005] In order to solve the technical problem that the existing technology only recommends the same category tags based on historical data, which is more accurately a retrieval-based push method that does not fully capture user preferences, and cannot perform effective data push when the same category tags do not appear in the data in the library, and this method is heavily dependent on the category tags of the data, and is prone to blind push, resulting in reduced user stickiness and affecting business operations, the present invention provides a precise push method and system based on user portraits.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a user profile-based precise push method, comprising:

[0009] S1: Get the category labels of each data content in the database;

[0010] S2: Obtaining user behavior data on the data content corresponding to each category tag, where the behavior data includes browsing data content category, browsing time, browsing interval, stay time and click frequency;

[0011] S3: Combine the behavioral data and the hidden Markov model to generate state transition probabilities and user preference labels. The state transition probability is the probability that the user will directly browse the data content of the second category label when browsing the data content of the first category label.

[0012] S4: Generate user profiles with push time periods based on behavioral data and user preference tags;

[0013] S5: Obtain the category label of the data content currently accessed by the user;

[0014] S6: Calculate the similarity between each category label and the category label of the data content currently accessed by the user using the improved cosine similarity with element sparsity;

[0015] S7: Generate the push data content for the next moment based on the category label of the data content currently accessed by the user, combined with the user portrait and similarity value;

[0016] S8: Record the length of time the user stays under the pushed data content;

[0017] S9: When the stay time is less than the preset stay time, a correction factor based on the difference between the stay time and the preset stay time is set to correct the state transition probability corresponding to the category label of the data content currently accessed by the user;

[0018] S10: Use the corrected state transition probability as the new state transition probability and return to step S5.

[0019] Second aspect

[0020] An embodiment of the present invention provides a user profile-based precision push system, comprising:

[0021] processor;

[0022] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the precise push method based on user portrait as described in the first aspect is implemented.

[0023] The third aspect

[0024] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for precise push based on user portrait as described in the first aspect is implemented.

[0025] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0026] In the present invention, the category labels of each data content in the database and the user's historical behavior data are first obtained, and then the hidden Markov model is introduced to perform data trend analysis on the user's historical behavior data based on the category labels, capturing user preferences and state transition probabilities. Based on the behavior data, it can fully explore the trend and probability of users transferring to different categories of data in the state of currently accessing data, and generate a user portrait with a push time period. Then, the improved cosine similarity with element sparsity is combined to calculate the similarity value between each category label and the category label of the data content currently accessed by the user. Even if the category label that the user wants to access does not exist, content that is extremely in line with the user's expectations can still be pushed based on the similarity value. This data-driven approach reduces the problem of heavy reliance on data category labels under data-driven approach, and can effectively expand push data according to user behavior. While ensuring that the push is in line with user behavior, it greatly improves the applicability of the push method, the push accuracy and the rationality of the push time, improves user stickiness, and increases business traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of a user profile-based precision push method provided by an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of the structure of a user profile-based precision push system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0031] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "example" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "example" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0032] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0033] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0034] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0035] Reference Manual Figure 1 , which shows a flow chart of a precise push method based on user portrait provided by an embodiment of the present invention.

[0036] The embodiment of the present invention provides a method for accurate push notifications based on user profiles. The method can be implemented by an accurate push notification device based on user profiles, which can be a terminal or a server. The processing flow of the accurate push notification method based on user profiles can include the following steps:

[0037] S1: Get the category labels of each data content in the database.

[0038] The data in the database is the data accessible to customers and can include product information, video information, audio information, text information, image information, and so on. The multiple categories in multi-category data content are essentially equivalent to category labels for the data content. For the data in the database, each data element is synchronously assigned a corresponding category label. Category labels can be assigned by the data content provider or generated based on existing algorithms. For data content in the database without category labels, product information and text information can be directly labeled using natural language processing techniques. Video data can be analyzed using computer vision techniques such as CNN to obtain video data content. Audio data can be extracted using MFCCs, and image information can be extracted using image recognition models. For non-text data content, category labels are ultimately extracted from the extracted data content using natural language processing techniques. Based on the obtained category labels, support vector machines or k-means clustering algorithms can be used to continuously classify the category labels until each data element in the database has a different descriptive label. In short, the deeper the classification, the more accurate the subsequent push notifications.

[0039] S2: Obtain the user's behavioral data on the data content corresponding to each category label.

[0040] Among them, behavioral data includes browsing data content category, browsing time, browsing interval, stay time and click frequency.

[0041] Browsing data content categories refer to the types of content users encounter during browsing, i.e., the data corresponding to category labels. Understanding which categories of content users tend to browse can help the system more accurately predict and recommend other content that users may be interested in. Browsing moments refer to the specific times when users engage in browsing activities, including date and time. This helps analyze user activity patterns. For example, some users may be more inclined to browse content in the morning or evening. User behavior patterns may vary at different times of the day, which is extremely important for optimizing push notification timing. Browsing interval refers to the time between two consecutive views of content with the same category label. Browsing intervals can reveal the user's stickiness with content with that category label. Dwell time refers to the time a user spends on a specific piece of content during a single browsing session, including looping videos or photo collections. Longer dwell times generally indicate higher user interest or satisfaction with the content and are an important indicator for evaluating content quality and user interest. Click frequency for the same content refers to the number of times a user clicks on a specific piece of content of the same type. This metric reflects the user's level of interest in or engagement with that type of content.

[0042] It should be noted that behavioral data is the basis for building accurate user portraits, which can help understand users' interests and behavioral habits. Collecting these detailed data enables the system to achieve higher personalized recommendation accuracy, thereby improving user satisfaction and engagement, optimizing user experience, and increasing potential revenue. By analyzing these behavioral data, the system can identify users' likes and dislikes, and adjust the content recommendation algorithm to ensure that the pushed content is more in line with the users' actual needs.

[0043] S3: Combine behavioral data and hidden Markov models to generate state transition probabilities and user preference labels.

[0044] The state transition probability is the probability that the user directly browses the data content of the second category label when browsing the data content of the first category label.

[0045] Among them, the hidden Markov model is a statistical model that assumes that the state of the system is "hidden". In other words, the system state is not directly visible, but can be inferred through observable variables (in our scenario, these variables are user behavior data), thereby describing a series of non-directly observable (hidden) states in a system, and the change process of these states has Markov properties. In step S3, the hidden Markov model analyzes the user's behavior data, generates state transition probabilities and user preference labels, and simulates the user's behavior pattern of transferring from one content category to another, thereby predicting the user's behavior trend, effectively processing time series data, capturing the dynamic changes in user behavior, and helping the push system to more accurately predict and adapt to user needs, and achieve more accurate content recommendations and personalized experience.

[0046] In a possible implementation, S3 specifically includes:

[0047] S301: Define the state element and observation element of the hidden Markov model, wherein the state element is the category label corresponding to the data content browsed by the user, and the observation element is the behavior data.

[0048] S302: Establishing an observation sequence based on behavioral data.

[0049] S303: Initialize the algorithm parameters of the hidden Markov model according to the observation sequence, where the algorithm parameters include the state transition probability matrix, the observation probability matrix and the initial state probability:

[0050] The state transition probability matrix contains the probability of transitioning from one state to another. The observation probability matrix refers to the probability of observing a specific observation outcome given a specific state. The initial state distribution refers to the probability that the user may be in each state at the beginning.

[0051] The specific elements in the state transition probability matrix are:

[0052]

[0053] Among them, a ij represents the state transition probability from category label i to category label j, q t Represents the state element at time t, i.e., the category label, T represents the total number of discrete moments in the observation sequence, P(q t =i,q t+1 =j|O,λ) represents the joint probability that the class label i is at time t and the class label j is transferred to time t+1 under the given observation sequence O and model parameter λ, P = (q t =i|O,λ) represents the probability of being in category label i at time t.

[0054] Among them, the category label actually refers to the hidden state.

[0055] The elements in the observation probability matrix are:

[0056]

[0057] Among them, b j (k) represents the probability of observation k when the class label is j, o t represents the observation value at time t in the observation sequence, P(q t =j,o t =k|λ) represents the joint probability of being in category label j and observing k at time t given the model parameter λ, P(q t =j|O,λ) represents the state element q at time t given the observation sequence O and model parameter λ. t is the overall occurrence probability of category label j, that is, the initial state probability.

[0058] Among them, observation probability is the core component of the hidden Markov model, which allows the model to interpret observed data based on assumptions about the current state. This is particularly important for behavioral data analysis because it can help the model accurately predict the external manifestations that may be observed under a specific internal state, and then be used for state inference and prediction of future behavior.

[0059] S304: Using the behavior data as input data of the hidden Markov model, training the hidden Markov model, and updating the state transition probability.

[0060] In a possible implementation, the update formula for the state transition probability is specifically:

[0061]

[0062] Among them, a' ij represents the updated state transition probability, α t (i) represents the forward probability at time t, i.e., the probability of the class label being i in the observation sequence from the beginning of the sequence to time t, b j (o t+1 ) means that o is observed when the category label is j t+1 The observation probability, β t+1 (j) represents the backward probability at time t+1, that is, the probability that the category label is j at time t+1 in the observation sequence O from time t+1 to the end of the sequence, and P(O|λ) represents the probability of occurrence of the observation sequence O under the given model parameters.

[0063] It should be noted that in the process of updating the state transition probability in the hidden Markov model, the forward probability, backward probability, observation probability and the probability of the entire observation sequence are combined to re-estimate the transition probability from one state to another. The model parameters can be dynamically adjusted according to the actual observed data, so that the model can more accurately reflect the true pattern of user behavior, improve the accuracy of the prediction and the model's adaptability to new data. This ability to continuously learn and adapt based on actual data is the key to maintaining the efficiency of the precise recommendation system in complex and ever-changing user behavior patterns.

[0064] S305: Given an observation sequence, a hidden state sequence based on the behavior data is generated through the trained hidden Markov model, wherein the state element of the hidden state sequence is the user preference label.

[0065] Specifically, first, the state elements (corresponding to the content category labels browsed by the user) and observation elements (user behavior data) of the model are defined in S301. Then, in S302, an observation sequence is constructed based on the behavior data, which provides the model with time series data input. In S303, the core parameters of the model are initialized: the state transition probability matrix, the observation probability matrix, and the initial state probability. These parameters determine how the model predicts transitions between states and responds to observed behaviors. Through the training process of S304, the behavior data is used to adjust the state transition probability of the model to ensure that the model can accurately map the actual pattern of user behavior. Finally, in S305, the model generates a hidden state sequence, which represents the sequence of user preference labels inferred from the observed behavior data.

[0066] This process enables a deep understanding and exploration of user behavior dynamics, as well as predictions of future trends. Using hidden Markov models, the system effectively captures the patterns of transitions between user behaviors, even those that appear random on the surface. Furthermore, dynamic adjustments to the model and updates to state transition probabilities ensure that the push notification system adapts to changes in user behavior over time, maintaining accurate and personalized recommendations and significantly enhancing user experience and satisfaction.

[0067] S4: Generate user profiles with push time periods based on behavioral data and user preference tags.

[0068] It should be noted that using the user preference tags and behavior data generated in the previous steps to create a user profile with a push time period not only reflects the user's content preferences, but also refines the optimal content push time, so that content push can be carried out during the time period when the user is most likely to be active. The advantage of this method is that it can significantly improve user interaction rate and satisfaction with content consumption, because the content push is highly consistent with the user's actual online time and preference for receiving information, thereby improving the effectiveness of the pushed content and the overall user experience. By accurately grasping the push timing, it is also possible to optimize resource utilization efficiency, reduce invalid pushes, and improve the return on marketing activities.

[0069] In a possible implementation, S4 specifically includes:

[0070] S401: Calculate the user activity index in each time period based on the behavior data:

[0071]

[0072] Among them, A(h) represents the user activity index in the time period h of the browsing time, N h represents the number of user activities in time period h collected based on browsing intervals, d i represents the duration of the i-th user activity, D represents the maximum duration of all user activities, k i represents the click frequency in the i-th user activity, and log represents logarithmic operation.

[0073] The time period can be in hours.

[0074] It should be noted that the logarithmic transformation of click frequency can help smooth out extreme values, ensuring that a single high-interaction activity does not have an excessive impact on the overall activity index, while maintaining the appropriate influence of low-level interactions.

[0075] S402: The time period corresponding to the maximum user activity index is used as the push time period:

[0076] T* =argmax h A(h)

[0077] Among them, T * Indicates the push time period.

[0078] S403: Each state element of the hidden Markov model under the observation sequence is used as the basic state of the user portrait, and each state element in the hidden state sequence, namely the user preference label, is used as the preference state of the user portrait. The line connecting the basic state and the preference state is the state transition probability between the two, and the push time period is added to the user portrait to complete the creation of the user portrait.

[0079] Specifically, the user activity index for each time period is calculated by analyzing user behavior data, taking into account the frequency, duration, and click frequency of user activities. The click frequency is logarithmically transformed to balance the influence of activities and prevent extreme values ​​from having an excessive impact on the overall activity index. Then, the time period with the highest activity index is selected as the optimal push time. The user behavior state (such as preferred content type) obtained through hidden Markov model analysis is integrated into the user profile. The transition probability between the basic state and the preferred state is linked, and the determined optimal push time period is added to the user profile. It can accurately capture and utilize the user's activity in a specific time period, thereby optimizing the timeliness and relevance of content push. By directly reflecting the user's behavior patterns and preferences in the user profile and combining them with real-time activity data, the push system can more intelligently decide when to display content to users, greatly improving user engagement and satisfaction. This not only enhances the user experience, but also optimizes resource allocation and improves operational efficiency by increasing content viewing and interaction rates.

[0080] S5: Obtain the category label of the data content currently accessed by the user.

[0081] S6: Calculate the similarity between each category label and the category label of the data content currently accessed by the user using the improved cosine similarity with element sparsity.

[0082] It should be noted that category labels are all text data, and text data is a sparse data, and most of the elements in the vector may be zero. In this case, even if two documents or labels have similar trends in several non-zero dimensions, their cosine similarity may be lowered due to the presence of a large number of zeros, which cannot truly reflect their similarity. By introducing element sparsity in cosine similarity, the sparsity problem can be considered in the process of calculating the similarity of category labels, and more accurate similarity value results can be obtained, thereby improving the accuracy of subsequent content push based on similarity. By considering the impact of data sparsity, the similarity between the category label of the content currently accessed by the user and other category labels in the database is calculated. It can more accurately handle the sparse characteristics of text data, thereby improving the accuracy of similarity calculation, which is particularly important for recommendation systems because it can ensure that even in the case of sparse data, the true similarity between content can be effectively identified, thereby improving the relevance of recommendations and user satisfaction.

[0083] In a possible implementation, S6 specifically includes:

[0084] S601: Vectorize each category label to obtain a vectorized category label.

[0085] S602: Introducing an element sparsity coefficient that is inversely proportional to the number of non-zero vectorized class labels:

[0086]

[0087] Where s represents the element sparsity coefficient, m represents the number of non-zero elements in the vectorized category labels, and M represents the total number of elements in the vectorized category labels.

[0088] S603: Calculate the similarity between any two category labels by combining the element sparsity coefficients of each category label:

[0089]

[0090] in, Represents the vectorized category labels of category label u and category label v respectively, Represents the similarity value between category label u and category label v, s u and s v Represents vectorized category labels and vectorized category labels The element sparsity coefficient of .

[0091] Among them, the square root is added in the calculation process of the similarity value mainly to alleviate the excessive adjustment of the similarity value by the sparsity factor. By using the square root, the adjustment effect of the sparsity factor can be made more gentle, avoiding the abnormal decrease in the similarity value caused by the two vectors being very sparse, thereby maintaining the accuracy and stability of the similarity measurement. Such processing not only makes the results more in line with the actual situation, but also enhances the applicability and explanatory power of the algorithm.

[0092] Specifically, by introducing the element sparsity coefficient, a common problem when processing sparse vector data is effectively solved. The sparsity coefficient increases the importance of non-zero elements when calculating similarity, allowing for more accurate evaluation and differentiation of category labels that are very sparse in the vectorized representation. This approach is particularly suitable for processing text data, as it can reduce the noise impact caused by the sparsity of high-dimensional data and improve the accuracy and efficiency of similarity calculations. In addition, taking into account the sparsity coefficient, this method can better capture the subtle differences between different category labels in large-scale datasets, helping to improve the strength of association between content pushed based on similarity values.

[0093] S7: Generate the push data content for the next moment based on the category label of the data content currently accessed by the user, combined with the user portrait and similarity value.

[0094] It can be understood that the system generates the push data content for the next moment based on the content category label currently accessed by the user, combined with the constructed user portrait and the similarity calculated through step S6. It can adapt to the user's latest interests in real time, and optimize content push by matching user portraits and content similarity, making recommended content more relevant and attractive to users. This not only improves user engagement and satisfaction, but also helps to enhance user loyalty and improve the overall user experience by accurately matching user needs and preferences.

[0095] In a possible implementation, S7 specifically includes:

[0096] S701: Generate an initial category label corresponding to the category label of the data content currently accessed by the user based on the state transition probability and the user portrait, wherein the initial category label is the category label with the highest state transition probability under the category label of the data content currently accessed by the user.

[0097] It should be noted that those skilled in the art can set the size of the preset state transition probability according to actual needs, and the present invention does not limit this.

[0098] S702: Calculate the category label with the largest similarity value with the initial category label as the expected category label.

[0099] S703: Output the data content corresponding to the expected category label as the pushed data content.

[0100] Specifically, the system first determines the most likely initial category label to which the user will transition based on the category labels of the content currently being accessed and the state transition probabilities. This step takes into account the user's historical behavior patterns to predict the next category the user is most likely to be interested in. The system then calculates the similarity between this initial category label and other category labels, selecting the category label with the highest similarity as the desired category label. Finally, the system pushes content corresponding to this desired category label to the user. This approach not only predicts and adapts to user behavior based on historical interaction data, avoiding situations where the pushed content is identical to the content being viewed, but also enhances the relevance and accuracy of content recommendations through similarity analysis. This approach dynamically expands the user's content consumption scope beyond content in which the user has shown direct interest, enabling the exploration and recommendation of new, relevant content, thereby enhancing the user experience, satisfaction, and engagement. Furthermore, the flexibility and adaptability of this strategy allows it to address changing user preferences and a wide range of content types, enhancing the platform's appeal and user stickiness.

[0101] It should be noted that this process not only makes full use of user browsing data, but also mines other expected category labels with higher similarity values ​​to user browsing data based on similarity values ​​while excluding the sparse influence. These other expected category labels can be existing data in the library or newly added. Finally, the expected category labels are obtained based on the similarity values, and then the corresponding data content is pushed accurately based on the expected category labels. In addition, the push type labels can be effectively expanded, and are not limited to the possible transfer type labels recorded by the state transition probability, because it is impossible for all type labels to be the same. This method effectively increases the applicable scenarios and scope of the solution.

[0102] S8: Record the length of time the user stays under the pushed data content.

[0103] S9: When the stay duration is less than the preset stay duration, a correction factor based on the difference between the stay duration and the preset stay duration is set to correct the state transition probability corresponding to the category label of the data content currently accessed by the user.

[0104] It should be noted that when the user's stay time is less than the preset stay time, the difference is used as a correction factor to adjust the state transition probability. By correcting these probabilities, the system can more accurately reflect the user's actual behavior and preference changes, and use the stay time as the driving data for the correction factor to continuously increase the user's stay time on the pushed content, forming a virtuous circle. Through dynamic adjustment, the pushed content is more in line with the user's current interests and reactions, thereby improving user engagement and content click-through rate. In addition, timely adjustment of push strategies can also help avoid user fatigue and improve user experience, making the content push system more efficient and accurate.

[0105] It should be noted that those skilled in the art can set the preset stay time according to actual needs, and the present invention does not limit this.

[0106] In a possible implementation, S9 specifically includes:

[0107] S901: A correction factor is set based on the difference between the stay duration and the preset stay duration. The correction factor is calculated as follows:

[0108]

[0109] Among them, γ(τ) represents the correction factor for the residence time τ, λ represents the decay rate, and τ target represents the preset stay duration, μ represents the average stay duration under the total number of stay duration records, and e represents the base of the exponential function.

[0110] Among them, the decay rate is directly related to the average length of stay of users, which is an intuitive engagement indicator. A shorter average length of stay means a higher decay rate, reflecting the user's rapid consumption of content or low engagement. A longer length of stay indicates a lower decay rate, showing the user's high investment and satisfaction with the content.

[0111] It should be noted that the correction factor increases as the stay time approaches the preset time. When the actual stay time is less than the preset time, this value will decrease, thereby reducing the state transition probability, and then making the stay time continue to tend towards the preset stay time, more accurately capturing the user's browsing behavior.

[0112] S902: Correct the state transition probability corresponding to the category label of the data content currently accessed by the user according to the correction factor:

[0113] A ij =γ(τ)×a ij

[0114] Among them, A ij represents the corrected state transition probability.

[0115] S10: Use the corrected state transition probability as the new state transition probability and return to step S5.

[0116] It is understandable that using the previously corrected state transition probability to update the state transition matrix of the entire model, this update process ensures that the model reflects the latest user behavior and preferences, making future content recommendations more accurate and personalized, and enabling the push method to continuously self-optimize and adjust its behavior through a real-time feedback loop, thereby improving the relevance of pushed content and user satisfaction, enhancing user experience, and ultimately improving long-term user engagement. This dynamic update mechanism provides support for ensuring the long-term effectiveness and adaptability of the push method, continuously dynamically updating and optimizing push content to achieve accurate content push.

[0117] In a possible implementation manner, after S10, the method further includes:

[0118] Update the data in the library at preset intervals.

[0119] It's important to note that updating the database data at preset intervals is designed to ensure the content library remains up-to-date, thereby increasing the relevance and appeal of content push. By regularly updating the library, the system can introduce new content or update existing content, ensuring that users are exposed to the latest and most relevant information. This not only helps maintain user interest and engagement, but also improves the platform's competitiveness and ensures the freshness and diversity of content.

[0120] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0121] In the present invention, the category labels of each data content in the database and the user's historical behavior data are first obtained, and then the hidden Markov model is introduced to perform data trend analysis on the user's historical behavior data based on the category labels, capturing user preferences and state transition probabilities. Based on the behavior data, it can fully explore the trend and probability of users transferring to different categories of data in the state of currently accessing data, and generate a user portrait with a push time period. Then, the improved cosine similarity with element sparsity is combined to calculate the similarity value between each category label and the category label of the data content currently accessed by the user. Even if the category label that the user wants to access does not exist, content that is extremely in line with the user's expectations can still be pushed based on the similarity value. This data-driven approach reduces the problem of heavy reliance on data category labels under data-driven approach, and can effectively expand push data according to user behavior. While ensuring that the push is in line with user behavior, it greatly improves the applicability of the push method, the push accuracy and the rationality of the push time, improves user stickiness, and increases business traffic.

[0122] Reference Manual Figure 2, which shows a structural diagram of a user portrait-based precise push system provided by the present invention.

[0123] The present invention further provides a user profile-based precise push system 20, which is applied to the above-mentioned user profile-based precise push method, comprising:

[0124] Processor 201.

[0125] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, a precise push method based on user portraits as in the method embodiment is implemented.

[0126] The user portrait-based precise push system 20 provided by the present invention can execute the above-mentioned user portrait-based precise push method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0127] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0128] In the present invention, the category labels of each data content in the database and the user's historical behavior data are first obtained, and then the hidden Markov model is introduced to perform data trend analysis on the user's historical behavior data based on the category labels, capturing user preferences and state transition probabilities. Based on the behavior data, it can fully explore the trend and probability of users transferring to different categories of data in the state of currently accessing data, and generate a user portrait with a push time period. Then, the improved cosine similarity with element sparsity is combined to calculate the similarity value between each category label and the category label of the data content currently accessed by the user. Even if the category label that the user wants to access does not exist, content that is extremely in line with the user's expectations can still be pushed based on the similarity value. This data-driven approach reduces the problem of heavy reliance on data category labels under data-driven approach, and can effectively expand push data according to user behavior. While ensuring that the push is in line with user behavior, it greatly improves the applicability of the push method, the push accuracy and the rationality of the push time, improves user stickiness, and increases business traffic.

[0129] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0130] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0131] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0132] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0133] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0134] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0135] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0136] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0140] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0141] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the precise push method based on user portrait as described in the method embodiment is implemented.

[0142] The computer-readable storage medium provided by the present invention can implement the steps and effects of the user portrait-based precise push method of the above-mentioned method embodiment. To avoid repetition, the present invention will not go into details.

[0143] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0144] In the present invention, the category labels of each data content in the database and the user's historical behavior data are first obtained, and then the hidden Markov model is introduced to perform data trend analysis on the user's historical behavior data based on the category labels, capturing user preferences and state transition probabilities. Based on the behavior data, it can fully explore the trend and probability of users transferring to different categories of data in the state of currently accessing data, and generate a user portrait with a push time period. Then, the improved cosine similarity with element sparsity is combined to calculate the similarity value between each category label and the category label of the data content currently accessed by the user. Even if the category label that the user wants to access does not exist, content that is extremely in line with the user's expectations can still be pushed based on the similarity value. This data-driven approach reduces the problem of heavy reliance on data category labels under data-driven approach, and can effectively expand push data according to user behavior. While ensuring that the push is in line with user behavior, it greatly improves the applicability of the push method, the push accuracy and the rationality of the push time, improves user stickiness, and increases business traffic.

[0145] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0146] There are a few points to note:

[0147] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0148] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0149] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0150] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A precise push method based on user portrait, characterized in that: include: S1: Get the category labels of each data content in the database; S2: Obtaining user behavior data on the data content corresponding to each category tag, wherein the behavior data includes browsing data content category, browsing time, browsing interval, stay time and click frequency; S3: Combining the behavioral data and the hidden Markov model, generating a state transition probability and a user preference label, wherein the state transition probability is the probability that the user directly browses the data content of the second category label when browsing the data content of the first category label; S4: Generate a user profile with a push time period based on the behavior data and the user preference tag; S5: Obtain the category label of the data content currently accessed by the user; S6: Calculating similarity values ​​between each category label and the category label of the data content currently accessed by the user using an improved cosine similarity with element sparsity; S7: Generate push data content for the next moment based on the category label of the data content currently accessed by the user, combined with the user portrait and the similarity value; S8: Recording the length of time the user stays under the pushed data content; S9: If the stay duration is less than the preset stay duration, a correction factor based on the difference between the stay duration and the preset stay duration is set to correct the state transition probability corresponding to the category label of the data content currently accessed by the user; S10: Use the corrected state transition probability as the new state transition probability and return to step S5.

2. The precise push method based on user portrait according to claim 1 is characterized in that: The S3 specifically includes: S301: defining the state element and observation element of the hidden Markov model, wherein the state element is the category label corresponding to the data content browsed by the user, and the observation element is the behavior data; S302: Establishing an observation sequence based on the behavior data; S303: Initialize the algorithm parameters of the hidden Markov model according to the observation sequence, wherein the algorithm parameters include a state transition probability matrix, an observation probability matrix, and an initial state probability: The elements in the state transition probability matrix are specifically: Among them, a ij represents the state transition probability from category label i to category label j, q t Represents the state element at time t, i.e., the category label, T represents the total number of discrete moments in the observation sequence, P(q t =i,q t+1 =j|O,λ) represents the joint probability that the class label i is at time t and the class label j is transferred to time t+1 under the given observation sequence O and model parameter λ, P = (q t =i|O,λ) represents the probability of being in category label i at time t; The elements in the observation probability matrix are specifically: Among them, b j (k) represents the probability of observation k when the class label is j, o t represents the observation value at time t in the observation sequence, P(q t =j,o t =k|λ) represents the joint probability of being in category label j and observing k at time t given the model parameter λ, P(q t =j|O,λ) represents the state element q at time t given the observation sequence O and model parameter λ. t is the overall occurrence probability of category label j, i.e. the initial state probability; S304: Using the behavior data as input data of the hidden Markov model, training the hidden Markov model, and updating the state transition probability; S305: Given the observation sequence, generate a hidden state sequence based on the behavior data through the trained hidden Markov model, wherein the state element of the hidden state sequence is the user preference label.

3. The user portrait-based precise push method according to claim 2, characterized in that: The update formula of the state transition probability is specifically: Among them, a' ij represents the updated state transition probability, α t (i) represents the forward probability at time t, i.e., the probability of the class label being i in the observation sequence from the beginning of the sequence to time t, b j (o t+1 ) means that o is observed when the category label is j t+1 The observation probability, β t+1 (j) represents the backward probability at time t+1, that is, the probability that the category label is j at time t+1 in the observation sequence O from time t+1 to the end of the sequence, and P(O|λ) represents the probability of occurrence of the observation sequence O under the given model parameters.

4. The user portrait-based precise push method according to claim 2, characterized in that: The S4 specifically includes: S401: Calculate the user activity index in each time period based on the behavior data: Wherein, A(h) represents the user activity index in the time period h of the browsing moment, N h represents the number of user activities within the time period h collected according to the browsing interval, d i represents the duration of the i-th user activity, D represents the maximum duration of all user activities, k i represents the click frequency in the i-th user activity, and log represents logarithmic operation; S402: The time period corresponding to the maximum user activity index is used as the push time period: T * =argmax h A(h) Among them, T * Indicates the push time period; S403: Each state element of the hidden Markov model under the observation sequence is used as the basic state of the user portrait, each state element in the hidden state sequence, namely the user preference label, is used as the preference state of the user portrait, the line connecting the basic state and the preference state is the state transition probability between the two, and the push time period is added to the user portrait to complete the creation of the user portrait.

5. The precise push method based on user portrait according to claim 1 is characterized in that: The S6 specifically includes: S601: Vectorize each category label to obtain a vectorized category label; S602: Introducing an element sparsity coefficient that is inversely proportional to the number of non-zero vectorized class labels: Wherein, s represents the element sparsity coefficient, m represents the number of non-zero elements in the vectorized category label, and M represents the total number of elements in the vectorized category label; S603: Calculate the similarity between any two category labels by combining the element sparsity coefficients of each category label: in, Represents the vectorized category labels of category label u and category label v respectively, Represents the similarity value between category label u and category label v, s u and s v Represents vectorized category labels and vectorized category labels The element sparsity coefficient of .

6. The user portrait-based precise push method according to claim 1, characterized in that: The S7 specifically includes: S701: Generate an initial category label corresponding to the category label of the data content currently accessed by the user based on the state transition probability and the user profile, wherein the initial category label is the category label with the highest state transition probability when the category label of the data content currently accessed by the user is the same as the category label of the data content currently accessed by the user; S702: Calculate the category label with the largest similarity value with the initial category label as the expected category label; S703: Output the data content corresponding to the expected category label as the pushed data content.

7. The user portrait-based precise push method according to claim 1, characterized in that: The S9 specifically includes: S901: Setting the correction factor based on the difference between the stay duration and the preset stay duration. The correction factor is calculated as follows: Among them, γ(τ) represents the correction factor for the residence time τ, λ represents the decay rate, and τ target represents the preset stay duration, μ represents the average stay duration under the total number of stay duration records, and e represents the base of the exponential function; S902: Correct the state transition probability corresponding to the category label of the data content currently accessed by the user according to the correction factor: A ij =γ(τ)×a ij Among them, A ij represents the corrected state transition probability.

8. The user portrait-based precise push method according to claim 1, characterized in that: After S10, the method further includes: The data in the library is updated at preset time intervals.

9. A precise push system based on user portrait, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the precise push method based on user portrait as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the user portrait-based precise push method as described in any one of claims 1 to 8 is implemented.

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