37 results about "Cold start recommendation" patented technology

The invention relates to the technical field of e-commerce, in particular to an e-commerce scene cold start recommendation method combining meta-learning and causal inference, and the method comprises the steps: obtaining user and commodity feature data, and carrying out the vectorization representation; analyzing an interactive causal relationship between user features and commodity features, constructing a causal graph, and quantifying causal influence factors of user behaviors on commodity recommendation; constructing a cold start recommendation model based on a meta-learning framework, and converting a cold start problem into a cross-task transfer learning problem; training a meta-model, and learning a general mode between different tasks; using influence factors obtained by causal inference as constraint conditions, and optimizing a target function of the meta-learning recommendation model; and designing a personalized recommendation strategy, and generating a personalized cold start recommendation result in combination with causal inference constraints. According to the method, meta-learning and causal inference are comprehensively utilized, the cold start problem in an e-commerce scene is effectively solved, and recommendation individuation and interpretation are improved.

The invention relates to the technical field of cold start, and discloses an image cold start recommendation sorting method and system, and the method comprises the steps: obtaining the three-dimensional point cloud data and RGB images of a target scene, and constructing a spatial topology layout relation graph, a fine-grained visual feature library and a multi-level feasibility constraint index vector; generating a multi-granularity representation vector based on the feature library, decomposing the multi-granularity representation vector into three types of independent attribute vectors through deentanglement learning, and generating a graph level representation vector; constructing a comprehensive scoring matrix, and outputting a final recommendation sequence through three-wheel progressive screening, multi-target Pareto sorting and visual confusion disambiguation; according to the method, the problems of visual feature semantic drifting and execution constraint mismatching in a cold start scene are effectively solved, and cognitive transition from visual similarity to function matching and then to an executable scheme is achieved.

The invention relates to a cold start recommendation method based on feature enhancement intention decoupling. A system model of the method comprises a feature enhancement module, an intention decoupling representation learning module, a cold start adaptation module and a recommendation prediction unit. The method comprises the following steps: firstly, carrying out adaptive weight learning on original features of a user and an article through a feature enhancement module, and dynamically adjusting the importance of different features; secondly, an improved capsule network is adopted to map the enhanced feature representation to a plurality of independent intention spaces, and fine-grained intention decoupling modeling is achieved; constructing an intention enhancement mechanism based on graph convolution, establishing an independent interaction sub-graph for each intention space, and learning intention-specific collaborative information; and finally, establishing a mapping relation from intention representation based on features to intention representation based on collaborative filtering through a cold start adaptation module, and realizing knowledge migration of a fine-grained intention level by adopting a bidirectional knowledge distillation strategy. According to the method, the problems of insufficient feature utilization, rough intention modeling and the like in traditional cold start recommendation are effectively solved, the recommendation accuracy and individuation degree are remarkably improved, and the method is particularly suitable for strict cold start scenes in which new users and new articles are completely lack of interaction history.

The invention belongs to the technical field of recommendation systems, and discloses a user cold start recommendation method based on meta-learning, and the recommendation technology comprises the detailed steps: step 1, user behavior log collection: collecting behavior data of a user on each platform, including behaviors of preference, resource browsing, clicking, searching and sharing; step 2, user feature preprocessing: key user features are selected based on feature distribution and significance indexes; and step 3, constructing a double-tower multi-target recall model: constructing the double-tower multi-target recall model based on the characteristics of the users and the articles. Through deep learning of consumption interest distribution of old users and correlation of articles, a more detailed and accurate new user portrait can be constructed, so that interests and preferences of the users can be matched more accurately; on the basis of accurate construction of the new user portrait, the recommendation system can formulate a personalized recommendation strategy, a one-by-one recommendation mode is avoided, and recommendation content better meeting user requirements is provided.

The invention relates to the technical field of cold start recommendation, in particular to a cold start recommendation method and system based on knowledge graph enhanced representation learning. Historical interaction behavior data of a user and an article and a structured knowledge graph are used as input data sources, entity relationship features, semantic attribute features and user interest distinguishing information are fused, and through a multi-stage feature modeling and sample optimization strategy, the problems of insufficient training signals and limited representation learning under a cold start condition are relieved. A structure and feature combined driven difficult negative sample mining mechanism is adopted, so that the sample discrimination is enhanced; in combination with a multi-hop neighbor feature aggregation method of relation specificity gating, semantic contribution weights of different relation types of neighbors to target node representation are effectively improved; and through a positive and negative view feature fusion strategy perceived by the interest boundary, discriminative modeling of the interest area and the non-interest area of the user is realized. And a dual-stage meta-learning optimization strategy is adopted, so that the rapid adaptation and generalization ability of the model to diversified cold start task distribution is improved.

The application provides a cold start recommendation method and a Thompson sampling recommendation method. The cold start recommendation method comprises the following steps: obtaining to-be-recommended information and a characteristic attribute of the to-be-recommended information; performing dimension classification on the characteristic attribute of the to-be-recommended information to obtain characteristic information of at least two dimensions; obtaining known information of a target user, the target user being a user without behavior data within a first preset time; selecting a plurality of recommended information from to-be-recommended information corresponding to each dimension characteristic information based on the association between the known information and the characteristic information of the at least two dimensions, and generating a recommendation pool based on the plurality of recommended information. The application performs information recommendation on the target user in the cold start period from the recommended information corresponding to the plurality of dimension characteristic information, improves the information recommendation hit rate, and enables the target user to quickly pass through the cold start period.

The present invention relates to the technical field of e-commerce, specifically to a cold start recommendation method for e-commerce scenarios that combines meta-learning and causal inference. The method includes: obtaining user and product feature data and performing vectorized representation; analyzing the interactive causal relationship between user features and product features, constructing a causal graph, and quantifying the causal influence factor of user behavior on product recommendation; constructing a cold start recommendation model based on the meta-learning framework, and transforming the cold start problem into a cross-task transfer learning problem; training the meta-model to learn the general patterns between different tasks; using the influence factor obtained by causal inference as a constraint condition to optimize the objective function of the meta-learning recommendation model; designing a personalized recommendation strategy, and generating personalized cold start recommendation results by combining causal inference constraints. The method of the present invention comprehensively utilizes meta-learning and causal inference, effectively solves the cold start problem in e-commerce scenarios, and improves the personalization and interpretability of recommendations.

The invention relates to a novel cold start recommendation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining novel data to be recommended and micro-drama data of a user, wherein the novel data to be recommended and the micro-drama data comprise information of the same type; based on an NLP model, respectively performing deep semantic feature extraction on the micro-drama data and the to-be-recommended novel data, and generating a micro-drama text vector and a novel text vector; based on a pre-trained multi-label model, performing label prediction according to the novel text vector, the micro-episode text vector and the acquired micro-episode label to obtain a novel prediction label; and through a hybrid recommendation engine, obtaining a semantic similarity score of the novel prediction tag and the micro-drama tag and a novel historical behavior score of the user, and generating a novel recommendation list for recommendation according to the semantic similarity score and the novel historical behavior score. According to the invention, the accuracy of novel recommendation can be improved.

The invention relates to a meta-learning cold start recommendation method based on multi-semantic space fusion, and the method comprises the steps: carrying out the feature extraction of the attribute content of a target user, and generating a first content feature vector; generating a corresponding path sequence of the target user under a preset meta path based on the heterogeneous information network, performing feature extraction based on the path sequence, and generating a corresponding first semantic association feature vector of the target user under the meta path; taking the first semantic association feature vector as an initial feature vector, constructing a corresponding hypergraph structure based on an interaction relationship of the target user under the meta-path, and performing multilayer convolution processing on the hypergraph structure to obtain a first high-order interaction feature vector corresponding to the target user; and fusing the first content feature vector, the first semantic association feature vector and the first high-order interaction feature vector, and inputting a fused target feature vector into a meta learning model to determine a target object recommended to a target user. According to the method, the recommendation accuracy in the cold start scene can be improved.

The present application provides a cold start recommendation method and related device. The method includes: respectively extracting the features of a target user, a first meta-path set, a second meta-path set, an item to be scored, and an item that has been scored, and mapping the extracted features of the first meta-path set, the second meta-path set, the item to be scored, and the item that has been scored to the same embedding space; determining a scoring offset of the item to be scored relative to the item that has been scored based on the features of the target user and the features mapped to the embedding space; determining a scoring prediction value of the target user for the item to be scored according to the scoring value of the target user for the item that has been scored and the scoring offset, so as to determine whether to recommend the item to be scored to the target user based on the scoring prediction value. Based on the features mapped to the embedding space and the features of the target user, the present application can determine an accurate scoring offset, and further, the scoring prediction value determined based on the scoring offset is more consistent with the true scoring value, and the recommendation result is more accurate.

The invention discloses a cold start recommendation method and device based on a preference recovery generative adversarial network and a readable medium, and the method comprises the steps: constructing a warm user score set and a cold start user score set based on a user score matrix, obtaining a warm score vector of a real user and a cold score vector of the real user according to the warm user score set and the cold start user score set, and converting the warm score vector of the real user into a generated cold score vector of the user by adopting a preference recovery function; a generative adversarial network is constructed and trained, the generative adversarial network comprises a generative network and a discrimination network, and the trained generative network is obtained; inputting cold score vectors of real users in the cold start user score set into the trained generative network to obtain predicted warm score vectors of the users, and performing Top-k recommendation according to the cold score vectors of the real users and the predicted warm score vectors of the users to obtain a recommendation item sequence of each user, therefore, the coverage rate and the accuracy are improved.

The present application belongs to the technical field of cold start of home maintenance recommendation system, and particularly relates to a household appliance maintenance cold start recommendation method based on an improved clustering algorithm and a Catboost model, comprising: fusing obtained community information and user information to form a new user portrait; statistically processing community users to divide the users into user group 1 and user group 2; for the user group 1, an improved clustering algorithm is used for clustering to obtain a similar user group, and the service with the most number of preferred products in the similar user group is recommended; for the user group 2, a Catboost model is used for classification prediction and recommendation according to the new user portrait. The present application recommends in a double-recommendation mode, uses easily-obtained community information as auxiliary information, expands the user dimension, and more information can make the classification model more easily learn the relationship between the characteristics and the classification prediction, thereby strengthening the accuracy of the classification prediction, so as to accurately recommend products for the users.

The invention discloses a cold start service recommendation method based on a multi-strategy pre-training model. The cold start service recommendation method comprises the following steps: step 1, constructing a hypergraph based on labels, extracting cold start service features, and constructing service embedding; step 2, obtaining enhanced service embedding based on hypergraph contrast learning of singular value decomposition; step 3, obtaining service embedding based on content optimization based on comparative learning of the description text; and 4, carrying out fine adjustment on the cold start recommendation model through a downstream recommendation task. The cold start service recommendation method based on the multi-strategy pre-training model is designed by analyzing and optimizing data features of Web APIs without historical interaction records and taking a hypergraph neural network as a technical support, and aims to solve the problem of recommendation of APIs without any historical interaction information and optimize the recommendation performance of services.

The invention discloses a cloud native scene cold start data service feature extraction method, which comprises the following steps of: 1, extracting a cold start service hypergraph based on label information; 2, processing cold start neighbor node data based on a meta aggregator; step 3, improving hypergraph aggregation operation based on an adaptive sampling strategy; 4, optimizing the parameters of the cold start encoder according to the embedding predicted by the hot start encoder; and 5, carrying out fine adjustment on the cold start recommendation model through a downstream recommendation task. According to the method, the recommendation problem of the pure cold start API without any historical interaction information is effectively solved.

This invention discloses a cold-start recommendation method and system based on dynamic adaptive fusion of multimodal features. First, a multimodal semantic graph is constructed to extract features. Second, cold-start items are identified by calculating the deviation between the number of item interactions and the average number of item interactions. Third, a neighborhood set of cold-start items is extracted, and its average neighborhood similarity in the multimodal semantic graph is calculated. Next, base weights are calculated based on the number of item interactions, and a dynamic index is calculated by combining the average neighborhood similarity to determine the final weights. Then, the enhanced interaction matrix is ​​symmetrically normalized, and multi-signal extraction is performed on the symmetrically normalized enhanced interaction matrix. The signals are then fused to obtain the final preference probability. Finally, a Top-K recommendation list is generated based on the final preference probability as the final recommendation result. This invention can achieve high-precision, low-latency, and highly reliable personalized recommendations in cold-start scenarios, and is suitable for large-scale real-time recommendation platforms such as e-commerce and content distribution.

The application discloses a recommendation system and method based on meta-learning graph neural network for cold start, and belongs to the technical field of network recommendation. The technical problem to be solved by the application is how to overcome the defect that the cold start user interaction data is sparse and cannot accurately dynamically model user preferences. The technical solution adopted is that the system comprises a two-part graph construction layer, a graph embedding layer, a sequence coding layer and a meta-learning layer. The method is as follows: obtaining interaction data in a data set, constructing a user-item two-part graph using the interaction data; using a graph convolution network based on the two-part graph to construct high-order relationships between items in sequences, and generating accurate embedding representations of users and items; using the sequence coding layer to learn the transfer information of items within the sequence, and combining an attention mechanism to generate a dynamic interest representation for the user; and using the meta-learning layer to quickly adapt to the cold start recommendation task.

The application belongs to the technical field of e-commerce commodity cold start recommendation, and discloses a commodity recommendation method of multi-granularity attribute set cooperative neighbor attention, first, a user-user similarity relation graph is constructed by using the multi-granularity attribute set of the user, a commodity-commodity similarity relation graph is constructed by using the multi-granularity attribute set of the commodity, and the cooperative neighbor embedding representation of the user and the commodity on the multi-granularity attribute set is learned through a graph neural network; then the cooperative neighbor embedding representation on the multi-granularity attribute set is fused through an attention mechanism, and the user embedding representation and the commodity embedding representation are modeled; the score of the user to the commodity is calculated through inner product, the preference of the user attribute to the commodity attribute is learned, and a ranked commodity recommendation list is generated according to the score size. The application mines the personalized interest of the user from the multi-granularity attribute combination of the user and the multi-granularity attribute combination of the commodity, realizes the commodity recommendation of the interactive behavior cold start, and provides great support especially in the aspects of e-commerce new user and new commodity recommendation.

This invention relates to the field of cold start technology and discloses an image cold start recommendation and ranking method and system. By acquiring 3D point cloud data and RGB images of the target scene, a spatial topology layout relationship graph, a fine-grained visual feature library, and multi-level feasibility constraint index vectors are constructed. Based on the feature library, multi-granularity representation vectors are generated, which are then decomposed into three independent attribute vectors through de-entanglement learning and graph-level representation vectors are generated. A comprehensive scoring matrix is ​​then constructed, and after three rounds of progressive screening, multi-objective Pareto ranking, and visual confusion disambiguation, the final recommendation sequence is output. This invention effectively solves the problems of visual feature semantic drift and execution constraint mismatch in cold start scenarios, achieving a cognitive leap from "visual similarity" to "functional matching" and then to "executable solutions."

The application discloses a social prompt cold start recommendation method based on an SPGCLRec framework, and comprises the following steps: step one, constructing an interaction graph and pre-training a recommendation model; step two, optimizing a social prompt based on a diffusion model; step three, enhancing a cold start user representation; and step four, generating a cold start recommendation and jointly optimizing a model. The learnable social soft prompt designed by the application embeds social information into a representation space of a pre-training model, and can realize efficient parameter adaptation without fine-tuning core weights of the model, thereby making up for the deficiency of the prior art which only relies on a single auxiliary clue and realizing efficient parameter adaptation of the pre-training model. The diffusion model is used to filter noise social relationships in a latent vector space, thereby improving user representation accuracy. By introducing a contrast learning mechanism and introducing Gaussian noise in a latent space, the semantic consistency of the optimized social prompt and the item representation is strengthened, and the robustness of the user representation is improved.

Recommendation services typically struggle with sparse data scenarios. A cold generated item start technique can find a warm generated item as a suitable proxy in a matrix representing co-occurrences of generated items. Embeddings can be used to determine distance between items. The technologies are useful for providing recommendations even in scenarios involving little or no transaction data.

The invention relates to the technical field of recommendation systems and multi-modal representation learning, in particular to a multi-modal recommendation semantic token modeling method based on residual vector quantization. The method aims at solving the problem that in a cold start scene, user-article interaction is sparse, multi-modal feature noise is large, alignment is difficult, and the recommendation effect is poor. According to the method, user behaviors, article images and text features are obtained, multi-modal features are mapped to a unified dimension and then input into a multi-level residual vector quantization self-encoding module, discrete semantic tokens are learned, collaborative filtering representation is obtained in combination with a user-article bigraph, fusion is carried out in a representation layer, and a user-article image is obtained. The user-article matching score is jointly optimized through the sorting-based loss function, the method is suitable for multi-modal recommendation systems such as e-commerce and content platforms, the accuracy and robustness of cold start recommendation can be improved, and the online calculation overhead can be reduced.

The invention belongs to the field of artificial intelligence recommendation systems, and particularly relates to a multi-scene recommendation method based on heterogeneous knowledge embedding fusion. According to the method, firstly, feature representation is carried out on user-article interaction data, meanwhile, modeling is carried out on entities and relationships in a knowledge graph by adopting the knowledge graph, and embedded representation of users, articles, entities and relationships is uniformly represented in a vector form, so that multi-scene recommendation is realized. According to the method, corresponding recommendation strategies are designed in three typical scenes of common recommendation, cold start recommendation and data sparse recommendation, so that the adaptability and generalization ability of a recommendation system are remarkably improved. Compared with an existing recommendation method which only depends on historical interaction records of the user, the method has the advantages that potential semantic information can be supplemented by utilizing the knowledge graph under the condition that user interaction data is insufficient or objects are not exposed, so that the cold start problem is relieved; under the condition of large-scale sparse interaction, the coverage and diversity of recommendation results are improved through a similarity modeling and knowledge completion mechanism.

The application discloses a user cold start recommendation system and method based on similarity matching, constructs a knowledge graph database according to users, products and the interaction relationship between the users and the products, and carries out standardization processing. The processed data is extracted to score information and mapped to a user like-dislike type space, so that the like-dislike product type of each user is determined, and a similarity matching algorithm is designed to find the old user with the highest similarity to the new user. The old user product type score vector and the new user product type vector score are processed by an association function and used as the recommendation basis of the system, the reasonable expansion of the new user data is completed, and the cold start problem of the recommendation system is effectively alleviated. The application solves the user cold start problem caused by the introduction of a new user into an industrial recommendation system. The application can be used for the scene of effectively recommending the product types that meet the preferences of new users in the industrial recommendation system.

The invention discloses a cold start recommendation method and device and a storage medium, and the method comprises the steps: obtaining a total data set generated when a user uses an application, and dividing the total data set into a training set and a test set; the training set is sent to a meta-learning framework for training, and a trained meta-learning framework is obtained; and sending the test set into the trained meta-learning framework for score prediction to obtain a score result, and generating a recommendation list according to the score result. According to the meta-learning framework, the problem of lack of sample data in cold start recommendation can be solved, so that cold start recommendation can be realized. The method can be widely applied to the technical field of cold start recommendation.

The invention discloses a recommendation cold start enhancement method based on large language model semantic interaction knowledge distillation. The recommendation cold start enhancement method is characterized by comprising the steps that a closed source large language GPT-4o model is used for constructing a semantic interaction graph between articles in an off-line mode; designing dual-channel comparison learning loss, and distilling structured semantic relationship knowledge to a lightweight sequence recommendation model; and based on the sequence recommendation model, searching an article with a high attention score in the sequence, replacing the article with a cold start article according to the semantic similarity, judging whether a certain article is the next interaction object of the user or not through a fine-tuned Tonyuqian 7B model, and generating supplementary training data for secondary training of the sequence recommendation model. Compared with the prior art, the problems of semantic drift, logic fracture and situation mismatch in cold start recommendation are effectively relieved, recommendation precision and interpretability in new users, new articles and new fields are improved, training efficiency and online reasoning compatibility are considered, and the method is suitable for multi-scene cold start recommendation tasks of e-commerce, social contact, content platforms and the like.

The invention discloses a user and article semantic feature generation method for cold start recommendation, and belongs to the technical field of recommendation systems for semantic modeling based on a large language model. According to the method, after preprocessing operation of data extraction, data cleaning, user behavior sequence construction and validity check, article basic label generation and user basic label generation are realized through online LLM; based on the article basic label dictionary and the user basic label dictionary, performing fine tuning by constructing a training data set and using a LoRA method, setting training parameters and a loss function to perform training circulation, and outputting a local LLM after fine tuning; by generating the article multi-role advanced semantic tag and generating the user multi-role cognitive advanced semantic tag and outputting the article multi-role advanced semantic tag and the user multi-role cognitive advanced semantic tag, the cold start problem in the recommendation system is solved through the application of the method in the recommendation system, and the recommendation efficiency is improved. And particularly, the cold start problem of sparse user interaction data is solved.

One or more embodiments of the invention provide a cold start recommendation method, a model training method, equipment, a storage medium and a product. The cold start recommendation method is applied to a first class client corresponding to a cold start user, and comprises the following steps: acquiring user information of the cold start user and project information of a to-be-recommended project; downloading the trained user representation model and the global item embedding vector of the item to be recommended from the server; wherein the user characterization model and the global item embedding vector are obtained by a plurality of second-class clients by utilizing respective local data sets to perform federated learning; converting the user information into a user representation vector by using a user representation model; and performing recommendation prediction on the to-be-recommended item based on the user information, the item information, the global item embedding vector and the user representation vector so as to complete a recommendation task for the to-be-recommended item based on a recommendation prediction result. And the recommendation precision is effectively improved by utilizing knowledge obtained by federal learning of the second-class client.

The invention discloses a recommendation method and system, and the method comprises the steps: S1, obtaining unbiased features and auxiliary features of a user, carrying out the embedding processing of the unbiased features and the auxiliary features, and obtaining feature embedding and other feature embedding; s2, clustering all users in groups through a clustering module; s3, based on the distance from the user to each cluster, distributing a target cluster for the user, and obtaining a group representation of the target cluster; S4, generating a complete user representation containing a clustering center and a personalized offset feature; s5, inputting the complete user representation into a main recommendation model, and outputting a recommendation result; and S6, constructing inter-cluster correlation regular loss and main model basic loss, and completing cold start recommendation of the low-activity users. According to the method, grouping clustering of the users is firstly completed through unbiased features, interest migration from high-activity users to low-activity users is achieved, and therefore cold start recommendation of the low-activity users is achieved.

The application discloses a user cold start recommendation method based on individual and group adaptability meta-learning, comprising: inputting item interaction information into a related user identification component and a prior-based interest extraction component respectively; identifying related users showing the same interest for a specified item through the related user identification component, identifying related users with similar preferences and sharing similar knowledge by using a meta-path in a heterogeneous information network, obtaining prior knowledge corresponding to the found group, and realizing user group level adaptation; extracting the correlation relationship between different interests of the user and historical task knowledge through the prior-based interest extraction component, obtaining individual prior knowledge, and realizing user individual level adaptation; and guiding the global shared prior knowledge to adapt to the knowledge of the user by constructing an adapter for the user. The application has the advantages of simple implementation method, good generalization effect, high recommendation efficiency and precision, etc.

The embodiment of the invention discloses an article cold start recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the steps of determining a to-be-recommended user data set and a to-be-recommended article data set; determining an embedded representation of each to-be-recommended article according to the to-be-recommended article data set; in response to an article recommendation instruction of a target user in the to-be-recommended user data set, determining a target behavior article corresponding to a target behavior of the target user, and generating a target behavior queue; determining the preference degree of the target user for each to-be-recommended article based on the embedded representation of each to-be-recommended article, the target behavior queue and a preset cold start condition; target to-be-recommended articles matched with the target user are determined based on the preference degree, and all the target to-be-recommended articles are recommended and displayed to the target user. According to the scheme provided by the embodiment of the invention, effective recommendation of cold start of the article can be realized, and the exposure degree of the new article is increased.