Big data intelligent recommendation method and system based on multi-scene dynamic adaptation

Abstract

The invention discloses a big data intelligent recommendation method and system based on multi-scene dynamic adaptation. A dynamic user portrait is generated by constructing a three-dimensional scene feature space and fusing multi-source heterogeneous data; scene-feature weight adaptive adjustment is realized based on reinforcement learning, and a multi-scene model is trained in combination with a'shared-private 'neural network and transfer learning; and finally, dynamically allocating resources according to the scene priority to generate a recommendation result, and continuously optimizing through a feedback closed loop. The problems that a traditional recommendation system is insufficient in scene adaptation, poor in real-time performance and the like are solved, the recommendation precision and the resource utilization rate are remarkably improved, and the method is suitable for multiple fields such as e-commerce and content distribution.

Description

Technical Field

[0001] This application relates to the fields of big data processing, reinforcement learning and intelligent recommendation systems, and in particular to a big data intelligent recommendation method and system based on dynamic adaptation across multiple scenarios. Background Technology

[0002] With the rapid development of mobile internet and big data technologies, recommendation systems have been widely applied in diverse scenarios such as e-commerce, content distribution, online education, and local life services. However, existing recommendation systems face significant technical bottlenecks in adapting to multiple scenarios:

[0003] Static scene modeling: It relies on a fixed feature space (such as user history behavior), without building a dynamic scene feature system, and cannot distinguish the differences in user needs in spatiotemporally heterogeneous scenarios (such as weekday commuting / weekend home), resulting in a disconnect between recommendation strategies and scene needs;

[0004] Insufficient granularity of data fusion: There are fusion barriers between offline data (historical behavior) and real-time data (current environment, operation sequence), failing to form a dynamic user profile of "long-term preferences - short-term needs", and the single data dimension leads to limited recommendation accuracy;

[0005] The real-time response mechanism is missing: the model parameter update cycle is on the hourly or daily level, which cannot capture the changes in demand brought about by scene switching (such as users switching from "4G commuting" to "WiFi office") in real time, and the response latency exceeds 500ms.

[0006] Weak cross-scenario transfer capability: The model is trained using an independent scenario model mode, which does not take advantage of the feature correlation between scenarios. In data-sparse scenarios (such as new category recommendations and holiday-specific scenarios), the recommendation accuracy drops by more than 40%.

[0007] Resource scheduling incompatibility: The adoption of a unified computing resource allocation strategy results in high-value scenarios (such as transaction conversion scenarios) sharing computing power with low-value scenarios (such as browsing scenarios), leading to a recommendation latency rate of over 15% in core scenarios. Summary of the Invention

[0008] Based on this, the embodiments of this application provide a big data intelligent recommendation method and system based on multi-scenario dynamic adaptation, which aims to solve the technical problems in existing recommendation systems such as lack of scene dynamic modeling, inefficient multi-source data fusion, delayed real-time response, poor cross-scenario mobility, and unreasonable resource scheduling.

[0009] Firstly, a big data intelligent recommendation method based on dynamic adaptation across multiple scenarios is provided, which includes:

[0010] S1. Construct a three-dimensional scene feature space based on user attributes, environmental parameters, and behavior sequences, and generate scene feature vectors; wherein, the scene feature vectors include at least user ID, scene ID, time features, location features, device features, behavior activity, and scene priority;

[0011] S2. The integrated stream and batch architecture is adopted to merge offline historical data with real-time scene data to generate a unified user profile containing static and dynamic profiles.

[0012] S3. Using the scene feature vector output by S1 and the unified user profile output by S2 as input, the scene-feature weight vector is obtained through online training of deep reinforcement learning (DQN). When switching scenes, the scene-feature weight vector is smoothly updated through the EMA algorithm to realize the direct mapping between scene demand changes and weight adjustments.

[0013] S4. Construct a shared-private two-layer neural network, and use knowledge distillation to transfer the parameters of the shared layer in the big data scenario to the private layer in the small data scenario to obtain a scenario-specific recommendation model.

[0014] S5. Dynamically allocate GPU / CPU computing resources based on the scene priority field of the scene feature vector, perform scene filtering, fine ranking and diversity rearrangement on candidate products, and output the final recommendation list;

[0015] S6. Real-time collection of user feedback on the recommendation list. If the scene-level click-through rate is lower than the threshold within a preset time interval, incremental updates are triggered and the backup model is rolled back to achieve closed-loop iteration.

[0016] Optionally, the behavior sequence dimension in S1 includes behavior type, behavior activity, and behavior sequence entropy; wherein the behavior activity is obtained by summing the frequency of operations per unit time × the behavior weight.

[0017] Optionally, the dynamic profile described in S2 encodes the behavioral sequence of the past 10 minutes using the LSTM algorithm to generate a short-term demand vector with fixed dimensions.

[0018] The short-term demand vector and the long-term preference vectors of age, gender, and frequently purchased categories in the static profile are concatenated in the memory snapshot by user ID primary key to form a unified user profile.

[0019] Optionally, the reward function of DQN described in S3 is R = α × click rate + β × conversion rate + γ × diversity score;

[0020] α, β, and γ are dynamic coefficients and their sum is 1. The system automatically adjusts the weights of the three coefficients every 10 minutes based on scenario-level business objectives.

[0021] Optionally, the shared-private dual-layer network described in S4 includes a ReLU-activated 3-layer fully connected shared layer and a scenario-specific GRU private layer;

[0022] After pre-training in a large data scenario, the shared layer transfers parameters to a new private layer with sparse data through the knowledge distillation temperature coefficient, thus achieving cross-scenario parameter transfer.

[0023] Optionally, the scenario priority field in S5 dynamically routes high-priority scenario requests to the GPU with a target response latency of ≤100ms; low-priority scenario requests are routed to the CPU with a target response latency of ≤300ms; the resource scheduler automatically adds or removes GPU / CPU instances every minute based on QPS fluctuations.

[0024] Optionally, the preset time interval mentioned in S6 is three consecutive 10-minute windows; when the scene-level click rate is lower than the threshold, the system pulls the feedback data of the most recent hour, performs incremental fine-tuning on the scene-specific GRU private layer in S4, and automatically switches to the backup model of the previous day to ensure that the recommendation effect is restored to stability within 30 minutes.

[0025] Secondly, a big data intelligent recommendation system based on dynamic adaptation across multiple scenarios is provided, which includes:

[0026] The construction module is used to construct a three-dimensional scene feature space based on user attributes, environmental parameters, and behavior sequences, and generate scene feature vectors; wherein, the scene feature vectors include at least user ID, scene ID, time features, location features, device features, behavior activity, and scene priority;

[0027] The fusion module is used to merge offline historical data with real-time scene data using a stream-batch integrated architecture to generate a unified user profile containing static and dynamic profiles.

[0028] The training module is used to take the output scene feature vector and the output unified user profile as input, and obtain the scene-feature weight vector W through deep reinforcement learning (DQN) online training. When switching scenes, W is smoothly updated through the EMA algorithm to realize the direct mapping between scene demand changes and weight adjustments.

[0029] The migration module is used to build a shared-private two-layer neural network. It uses knowledge distillation to transfer the parameters of the shared layer in big data scenarios to the private layer in small data scenarios, thus obtaining a scenario-specific recommendation model.

[0030] The allocation module is used to dynamically allocate GPU / CPU computing resources based on the scene priority field of the scene feature vector, perform scene filtering, fine ranking and diversity rearrangement on candidate products, and output the final recommendation list.

[0031] The iteration module is used to collect user feedback on the recommendation list in real time. If the scene-level click rate is lower than the threshold within a preset time interval, it will trigger an incremental update and roll back the backup model to achieve closed-loop iteration.

[0032] Thirdly, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the big data intelligent recommendation method based on dynamic adaptation across multiple scenarios as described in the first aspect above.

[0033] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the big data intelligent recommendation method based on dynamic adaptation across multiple scenarios as described in any of the first aspects above.

[0034] The beneficial effects of the technical solutions provided in this application include at least the following:

[0035] 1. Improved scene adaptation accuracy: Through 3D scene feature modeling and dynamic weight adjustment, the recommendation accuracy is improved by 35%-40% compared with traditional methods;

[0036] 2. Enhanced real-time response capability: The integrated stream and batch architecture and incremental model updates reduce the scene switching response latency to ≤100ms, meeting the needs of fragmented scenarios;

[0037] 3. Cross-scenario transferability optimization: Transfer learning strategies improve recommendation accuracy by 25%-30% in low-data-volume scenarios, solving the data sparsity problem;

[0038] 4. Optimized resource utilization efficiency: Dynamic resource scheduling reduces the overall system load by 45%, and the recommendation latency rate in core scenarios is reduced to below 5%;

[0039] 5. Excellent business scalability: The modular design supports the addition of new scenario types (such as AR interactive scenarios) and data dimensions (such as user physiological characteristics) to adapt to business growth needs. Attached Figure Description

[0040] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0041] Figure 1 A flowchart illustrating the steps of a big data intelligent recommendation method based on dynamic adaptation across multiple scenarios, provided in this application embodiment;

[0042] Figure 2A flowchart of a big data intelligent recommendation method based on dynamic adaptation across multiple scenarios, provided for embodiments of this application;

[0043] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] In the description of this application, the terms "comprising," "having," and any variations thereof are intended to cover non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those steps or units that are expressly listed, but may also include other steps or units that are not expressly listed but are inherent to these processes, methods, products, or apparatuses, or steps or units added based on further optimizations conceived in this application.

[0046] This invention aims to solve the problems of insufficient accuracy in bloodline analysis, low query efficiency, and weak multi-source adaptability in existing technologies. It provides a big data bloodline management system based on multi-dimensional features, which realizes complete tracking of bloodlines at the field level, shortens the query response time to the millisecond level, and supports rapid access to multiple types of data sources.

[0047] This invention aims to solve the technical problems in existing recommendation systems, such as "lack of dynamic scene modeling, inefficient multi-source data fusion, delayed real-time response, poor cross-scene mobility, and unreasonable resource scheduling," and provides a big data intelligent recommendation method that can perceive scene changes in real time and adaptively adjust recommendation strategies.

[0048] Please refer to Figure 1 The document illustrates a flowchart of a big data intelligent recommendation method based on dynamic adaptation across multiple scenarios, as provided in an embodiment of this application. This method may include the following steps:

[0049] S1. Construct a three-dimensional scene feature space based on user attributes, environmental parameters, and behavior sequences, and generate scene feature vectors.

[0050] The scene feature vector includes at least user ID, scene ID, time feature, location feature, device feature, behavior activity level, and scene priority. The behavior sequence dimension includes behavior type, behavior activity level, and behavior sequence entropy value; wherein, the behavior activity level is obtained by summing the frequency of operations per unit time multiplied by the behavior weight.

[0051] S2. The integrated stream and batch architecture is adopted to merge offline historical data with real-time scene data to generate a unified user profile containing static and dynamic profiles.

[0052] The dynamic profile uses the LSTM algorithm to encode the behavioral sequence of the past 10 minutes, generating a short-term demand vector with fixed dimensions. This short-term demand vector is then concatenated with the long-term preference vectors for age, gender, and frequently purchased categories from the static profile in a memory snapshot, using the user ID as the primary key, to form a unified user profile.

[0053] S3. Using the scene feature vector output by S1 and the unified user profile output by S2 as input, the scene-feature weight vector is obtained through online training of deep reinforcement learning (DQN). When switching scenes, the scene-feature weight vector is smoothly updated through the EMA algorithm, realizing the direct mapping between scene demand changes and weight adjustments.

[0054] Wherein, the reward function of DQN is R = α × click rate + β × conversion rate + γ × diversity score;

[0055] α, β, and γ are dynamic coefficients and their sum is 1. The system automatically adjusts the weights of the three coefficients every 10 minutes based on scenario-level business objectives.

[0056] S4. Construct a shared-private two-layer neural network and use knowledge distillation to transfer the parameters of the shared layer in the big data scenario to the private layer in the small data scenario to obtain a scenario-specific recommendation model.

[0057] The shared-private dual-layer network consists of a 3-layer fully connected shared layer with ReLU activation and a scene-specific GRU private layer. After pre-training in a large-scale data scenario, the shared layer transfers parameters to the new scene-sparse private layer through knowledge distillation temperature coefficient, achieving cross-scenario parameter transfer.

[0058] S5. Dynamically allocate GPU / CPU computing resources based on the scene priority field of the scene feature vector, perform scene filtering, fine ranking and diversity rearrangement on candidate products, and output the final recommendation list.

[0059] The scenario priority field dynamically routes high-priority scenario requests to the GPU, with a target response latency of ≤100ms; low-priority scenario requests are routed to the CPU, with a target response latency of ≤300ms; the resource scheduler automatically adds or removes GPU / CPU instances every minute based on QPS fluctuations.

[0060] S6. Real-time collection of user feedback on the recommendation list. If the scene-level click-through rate is lower than the threshold within a preset time interval, incremental updates are triggered and the backup model is rolled back to achieve closed-loop iteration.

[0061] The preset time interval is three consecutive 10-minute windows. When the scene-level click-through rate is lower than the threshold, the system pulls the feedback data from the most recent hour, performs incremental fine-tuning on the scene-specific GRU private layer in S4, and automatically switches to the backup model from the previous day to ensure that the recommendation effect is restored to stability within 30 minutes.

[0062] The following provides an optional specific implementation process of an embodiment of this application, such as... Figure 2 A flowchart of a big data intelligent recommendation method based on dynamic adaptation across multiple scenarios is presented. The method achieves dynamic adaptation across multiple scenarios through a closed-loop process of "scenario perception - data fusion - model training - recommendation generation - feedback optimization". The specific steps are as follows (flow logic: scenario feature extraction → multi-source data fusion → dynamic weight adjustment → model training → recommendation generation → feedback iteration):

[0063] 1. Construction of a multi-dimensional scene feature system

[0064] Key features are extracted from three dimensions: “user, environment, and behavior”, to generate scene feature vectors for accurate description of the current scene.

[0065] Implementation details include:

[0066] User dimension: Record user ID and membership level, and extract high-frequency keywords (such as "breakfast" and "portable") by analyzing click / search behavior over the past 3 months as long-term preference tags.

[0067] Environmental dimensions include time (divided into 4 time periods: early morning, morning, afternoon, and evening, with markings for holidays), location (using latitude and longitude to determine the business district type, such as subway or office building), device status (phone / tablet, 4G / WiFi network, battery level below 30%), and weather (sunny / rainy, temperature range).

[0068] Behavioral dimension: Record the user's current behavior type (browsing, searching, adding to cart, placing an order), calculate the behavior activity (weighted number of operations within 10 minutes, with ordering having the highest weight and browsing having the lowest), and determine the stability of the behavior (whether the operations are concentrated on a certain type of product).

[0069] Scene Feature Vector: Integrates the above information, in the format of [User ID, Scene ID, Time Feature, Location Feature, Device Feature, Behavioral Activity, Scene Priority (Levels 1-5, with Transaction Scenes being the highest)].

[0070] 2. Real-time fusion of multi-source heterogeneous data

[0071] By using the method of "storing history offline and transmitting dynamic data in real time", data can be quickly integrated to generate "static + dynamic" user profiles.

[0072] Implementation details include:

[0073] Offline data: User behavior records for the past 3 months are stored in Hive (partitioned by user and date), and product attributes (category, price, etc.) are stored in MySQL, making it easy to query historical preferences.

[0074] Real-time data: User's current actions (such as clicks and searches) are transmitted in real time via Kafka (supporting 200,000 records per second), and environmental information (weather, location) is synchronized every 5 minutes via API.

[0075] Dynamic profile generation: Flink is used to associate users with scenarios in real time. Static profiles retain basic user information (age, gender) and long-term preferences (frequently purchased categories). Dynamic profiles process the behavior of the past 10 minutes through the model to generate short-term needs (such as "urgently need breakfast delivered within 8 minutes").

[0076] 3. Scene-feature weight dynamic adaptation

[0077] Reinforcement learning is used to automatically adjust feature weights (such as the importance of "price" and "delivery time"), making recommendations more relevant to the current scenario, and the weights change smoothly when the scenario changes.

[0078] Implementation details include:

[0079] Weighting is adjusted dynamically based on recommendation performance (click-through rate, order conversion rate, product diversity). For example, in transaction scenarios, the weight of "delivery time" is increased, while the weight of "price" is decreased; in browsing scenarios, the weight of "diversity" is higher.

[0080] Smooth transition: When switching scenes (such as from commuting to home), the algorithm controls the magnitude of weight changes (no more than 20% per minute) to avoid sudden changes in recommendations.

[0081] Constraints: Each adjustment should be controlled within ±10% to prevent a certain feature (such as price) from having too high or too low a weight.

[0082] 4. Multi-scenario model training based on transfer learning

[0083] A two-tiered model of "shared + private" is built to take into account both general rules across scenarios and scenario-specific needs, thus solving the problem of inaccurate recommendations in scenarios with limited data.

[0084] Implementation details include:

[0085] Model structure: A shared layer (3-layer neural network) learns general features (such as users' general preference for "cost-effectiveness"); each scenario is equipped with a private layer (simple neural network) to learn scenario-specific features (such as users' preference for "short-time delivery" in commuting scenarios).

[0086] Knowledge transfer: The parameters of the shared layer trained in scenarios with abundant data (such as daily shopping) can be reused in scenarios with less data (such as holiday-specific scenarios) to quickly improve the recommendation effect in new scenarios.

[0087] Training method: Fine-tune the model with new data every 10 minutes (incremental update), and retrain with the full data once a day (full update) to avoid overfitting.

[0088] 5. Recommendation generation and dynamic resource scheduling

[0089] The system filters and sorts candidate products to generate final recommendations, and allocates computing resources according to the importance of the scenario to ensure response speed.

[0090] Implementation details include:

[0091] Candidate product generation: Based on the user's historical clicks, find similar products (e.g., recommend similar drinks if the user has bought milk), and initially select 200 candidate products.

[0092] Scene filtering: exclude unsuitable products based on scene characteristics (e.g., do not recommend video products when the battery is low to avoid consuming power).

[0093] Ranking and diversity: Use a scenario-specific model to score and select the top 50. Then adjust to ensure that the same category does not exceed 30% (e.g., a maximum of 3 of the same category in 10 recommendations), and finally output 10 products.

[0094] Resource scheduling: GPU acceleration is used for critical scenarios (such as transactions), with response time controlled within 100ms; CPU is used for general scenarios (such as browsing), with response time controlled within 300ms.

[0095] 6. Feedback loop and model iteration

[0096] We collect user feedback in real time, continuously optimize the model, and quickly switch to backup solutions in case of anomalies to ensure stable recommendations.

[0097] Implementation details include:

[0098] Feedback collection: Record user behavior (clicks, orders, etc.) in the database for evaluating the effectiveness of the recommendations.

[0099] Offline optimization: Analyze all data daily. If the click-through rate for a certain scenario remains below 2%, retrain the private model for that scenario.

[0100] Anomaly Handling: Monitor recommendation metrics in real time. If the performance is abnormal for 30 consecutive minutes (3 10-minute windows), update the model with the data from the past hour and switch to the backup model from the previous day to prevent recommendations from failing.

[0101] In addition, taking "commuting scenario product recommendations" as an example, the implementation process is explained in detail:

[0102] 1. Scene feature construction: User B (30 years old, office worker) commutes on the subway at 7:30 am on Wednesday, using a 4G network mobile phone (20% battery), and continuously browses the "breakfast" category. Behavior intensity = 4 (2 searches + 2 browsing), scene ID = "commuting - morning rush hour - low battery", priority = 3;

[0103] 2. Data Fusion: Offline profile shows that user B frequently buys "sandwiches" and "hot drinks" during the morning rush hour in the past 3 months; real-time data captures the current search keyword "ready-to-eat breakfast" and the subway arrival countdown of 8 minutes; dynamic profile shows that the short-term demand is "portable breakfast that can be ordered within 8 minutes";

[0104] 3. Weight Adjustment: The DQN algorithm strengthens the weight of the "delivery time" feature to 0.7 (initially 0.5) based on historical feedback, and reduces the weight of "price" to 0.1 (initially 0.3).

[0105] 4. Model Invocation: Activate the private sub-model of "commuting scenario", reuse user preference features of "daily diet" scenario in the shared layer, and strengthen the features of "portability" and "short-term delivery" in the private layer;

[0106] 5. Recommendation Generation: After the candidate set is filtered by "delivery time ≤ 30 minutes", it is scored by "delivery time (0.7) + category matching degree (0.2) + price (0.1)" to output the TOP10 products such as "ready-to-eat sandwiches" and "bottled hot soy milk", with the proportion of the same brand ≤ 20%;

[0107] 6. Feedback optimization: After user B places an order for "ready-to-eat sandwiches", the system updates the weight of the "sandwich" category in this scenario in real time, and incorporates the sample into the model training during offline iteration the next day.

[0108] This application also provides a big data intelligent recommendation system based on dynamic adaptation across multiple scenarios, which may include:

[0109] The construction module is used to construct a three-dimensional scene feature space based on user attributes, environmental parameters, and behavior sequences, and generate scene feature vectors; wherein, the scene feature vectors include at least user ID, scene ID, time features, location features, device features, behavior activity, and scene priority;

[0110] The fusion module is used to merge offline historical data with real-time scene data using a stream-batch integrated architecture to generate a unified user profile containing static and dynamic profiles.

[0111] The training module is used to take the output scene feature vector and the output unified user profile as input, and obtain the scene-feature weight vector W through deep reinforcement learning (DQN) online training. When switching scenes, W is smoothly updated through the EMA algorithm to realize the direct mapping between scene demand changes and weight adjustments.

[0112] The migration module is used to build a shared-private two-layer neural network. It uses knowledge distillation to transfer the parameters of the shared layer in big data scenarios to the private layer in small data scenarios, thus obtaining a scenario-specific recommendation model.

[0113] The allocation module is used to dynamically allocate GPU / CPU computing resources based on the scene priority field of the scene feature vector, perform scene filtering, fine ranking and diversity rearrangement on candidate products, and output the final recommendation list.

[0114] The iteration module is used to collect user feedback on the recommendation list in real time. If the scene-level click rate is lower than the threshold within a preset time interval, it will trigger an incremental update and roll back the backup model to achieve closed-loop iteration.

[0115] Specific limitations regarding the big data intelligent recommendation system based on multi-scenario dynamic adaptation can be found in the limitations of the big data intelligent recommendation method based on multi-scenario dynamic adaptation mentioned above, and will not be repeated here. Each module in the aforementioned big data intelligent recommendation system based on multi-scenario dynamic adaptation can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0116] In one embodiment, an electronic device is provided, which may be a computer, and its internal structure diagram may be as follows: Figure 3 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database of the computer device is used for intelligent recommendation data based on dynamic adaptation across multiple scenarios. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent recommendation method based on dynamic adaptation across multiple scenarios.

[0117] Those skilled in the art will understand that, Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0118] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described big data intelligent recommendation method based on dynamic adaptation across multiple scenarios.

[0119] In one embodiment of this application, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the above-described big data intelligent recommendation method based on dynamic adaptation across multiple scenarios.

[0120] The computer-readable storage medium and computer program product provided in this embodiment are similar in implementation principle and technical effect to the above method embodiments, and will not be repeated here.

[0121] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A big data intelligent recommendation method based on dynamic adaptation across multiple scenarios, characterized in that, The method includes: S1. Construct a three-dimensional scene feature space based on user attributes, environmental parameters, and behavior sequences, and generate scene feature vectors; wherein, the scene feature vectors include at least user ID, scene ID, time features, location features, device features, behavior activity, and scene priority; S2. The integrated stream and batch architecture is adopted to merge offline historical data with real-time scene data to generate a unified user profile containing static and dynamic profiles. S3. Using the scene feature vector output by S1 and the unified user profile output by S2 as input, the scene-feature weight vector is obtained through online training of deep reinforcement learning (DQN). When switching scenes, the scene-feature weight vector is smoothly updated through the EMA algorithm to realize the direct mapping between scene demand changes and weight adjustments. S4. Construct a shared-private two-layer neural network, and use knowledge distillation to transfer the parameters of the shared layer in the big data scenario to the private layer in the small data scenario to obtain a scenario-specific recommendation model. S5. Dynamically allocate GPU / CPU computing resources based on the scene priority field of the scene feature vector, perform scene filtering, fine ranking and diversity rearrangement on candidate products, and output the final recommendation list; S6. Real-time collection of user feedback on the recommendation list. If the scene-level click-through rate is lower than the threshold within a preset time interval, incremental updates are triggered and the backup model is rolled back to achieve closed-loop iteration.

2. The big data intelligent recommendation method according to claim 1, characterized in that, In S1, the behavior sequence dimension includes behavior type, behavior activity, and behavior sequence entropy; wherein, the behavior activity is obtained by summing the frequency of operations per unit time × the behavior weight.

3. The big data intelligent recommendation method according to claim 1, characterized in that, The dynamic profile described in S2 uses the LSTM algorithm to encode the behavioral sequence of the past 10 minutes to generate a short-term demand vector with fixed dimensions. The short-term demand vector and the long-term preference vectors of age, gender, and frequently purchased categories in the static profile are concatenated in the memory snapshot by user ID primary key to form a unified user profile.

4. The big data intelligent recommendation method according to claim 1, characterized in that, The reward function for DQN described in S3 is R = α × click-through rate + β × conversion rate + γ × diversity score; α, β, and γ are dynamic coefficients and their sum is 1. The system automatically adjusts the weights of the three coefficients every 10 minutes based on scenario-level business objectives.

5. The big data intelligent recommendation method according to claim 1, characterized in that, The shared-private two-layer network described in S4 includes a ReLU-activated 3-layer fully connected shared layer and a scenario-specific GRU private layer; After pre-training in a large data scenario, the shared layer transfers parameters to a new private layer with sparse data through the knowledge distillation temperature coefficient, thus achieving cross-scenario parameter transfer.

6. The big data intelligent recommendation method according to claim 1, characterized in that, The scenario priority field described in S5 dynamically routes high-priority scenario requests to the GPU, with a target response latency of ≤100ms; low-priority scenario requests are routed to the CPU, with a target response latency of ≤300ms; the resource scheduler automatically adds or removes GPU / CPU instances every minute based on QPS fluctuations.

7. The big data intelligent recommendation method according to claim 1, characterized in that, The preset time interval mentioned in S6 is three consecutive 10-minute windows; when the scene-level click rate is lower than the threshold, the system pulls the feedback data of the most recent hour, performs incremental fine-tuning on the scene-specific GRU private layer in S4, and automatically switches to the backup model of the previous day to ensure that the recommendation effect is restored to stability within 30 minutes.

8. A big data intelligent recommendation system based on dynamic adaptation across multiple scenarios, characterized in that, The system includes: The construction module is used to construct a three-dimensional scene feature space based on user attributes, environmental parameters, and behavior sequences, and generate scene feature vectors; wherein, the scene feature vectors include at least user ID, scene ID, time features, location features, device features, behavior activity, and scene priority; The fusion module is used to merge offline historical data with real-time scene data using a stream-batch integrated architecture to generate a unified user profile containing static and dynamic profiles. The training module is used to take the output scene feature vector and the output unified user profile as input, and obtain the scene-feature weight vector W through deep reinforcement learning (DQN) online training. When switching scenes, W is smoothly updated through the EMA algorithm to realize the direct mapping between scene demand changes and weight adjustments. The migration module is used to build a shared-private two-layer neural network. It uses knowledge distillation to transfer the parameters of the shared layer in big data scenarios to the private layer in small data scenarios, thus obtaining a scenario-specific recommendation model. The allocation module is used to dynamically allocate GPU / CPU computing resources based on the scene priority field of the scene feature vector, perform scene filtering, fine ranking and diversity rearrangement on candidate products, and output the final recommendation list. The iteration module is used to collect user feedback on the recommendation list in real time. If the scene-level click rate is lower than the threshold within a preset time interval, it will trigger an incremental update and roll back the backup model to achieve closed-loop iteration.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the big data intelligent recommendation method based on dynamic adaptation across multiple scenarios as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the big data intelligent recommendation method based on dynamic adaptation across multiple scenarios as described in any one of claims 1 to 7.

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