164results about How to "Improve recommendation effect" patented technology

The invention discloses an e-commerce platform commodity recommendation method and system based on user preference analysis, and relates to the technical field of e-commerce platforms, and the method comprises the steps: constructing a user multi-modal interest feature vector; the multi-modal interest feature vector fuses the text search history, the image browsing preference, the audio consumption habit and the video interaction behavior of the user; utilizing a cross-modal attention mechanism to match the multi-modal interest feature vector of the user with multi-modal attribute information of commodities to generate a candidate commodity set; and applying a dynamic context-aware reinforcement learning model to reorder the candidate commodity set according to real-time interaction behaviors of the user and environmental factors, and outputting a final recommendation result. According to the method, multi-modal interest feature extraction, dynamic context perception and reinforcement learning technologies are combined, so that comprehensive capture and accurate modeling of user interests are realized.

The invention provides an advertisement recommendation method and system based on a fusion neural network. The method comprises the steps of receiving real-time behavior data of a user; based on the real-time behavior data, a user interest map is dynamically constructed, and the user interest map comprises interested nodes of the user and the incidence relation between the interested nodes; analyzing the user interest map in real time by using a fusion neural network model; screening out a high-correlation advertisement set as candidate advertisements according to the preference probability; and in combination with the current scene in which the user is located, selecting a target advertisement from the candidate advertisements for pushing so as to improve the click rate and the conversion rate of the target advertisement. According to the technical scheme provided by the invention, the advertisement recommendation effect can be effectively improved.

The invention belongs to the technical field of information recommendation, and discloses a financial information recommendation method and system based on multi-channel distribution, and the method comprises the steps: collecting behavior data of multiple channels of a user, carrying out the cross-platform data integration, and forming a complete user behavior data set; constructing a multi-dimensional user portrait, establishing a financial information database, and marking a keyword tag for each piece of financial information; according to the keyword matching degree of the user portrait and the information content, using content recommendation and financial information conforming to user preferences, and based on historical behavior data of the user and behavior habits of similar users, using collaborative filtering to recommend financial information in which the user is interested to obtain a financial information recommendation list; according to the use habits of the user at different terminals, cross-platform information distribution is performed on the financial information recommendation list through multiple channels, so that the accurate recommendation capability of financial information is improved, and the user experience is improved.

The invention provides a recommendation strategy generation method based on big language model enhancement and related equipment. The method comprises the steps of obtaining a recommendation task; inputting the recommendation task into an intelligent agent, and obtaining a recommendation strategy output by the intelligent agent; wherein the intelligent agent is obtained by training based on a sample recommendation task and a large language model, and the large language model is used for predicting user behaviors based on the sample recommendation task. According to the method, the big language model is combined with the reinforcement learning agent, so that the problem that an accurate recommendation strategy is difficult to quickly provide by a traditional method is effectively solved, and the efficiency and the adaptability of a recommendation system are improved while the accuracy and the individuation degree of the recommendation strategy are improved.

The invention discloses a privacy protection-oriented personalized federal recommendation system and method, and relates to the field of federal recommendation systems. According to the method, an article preference embedding vector is constructed for each client, and a global article embedding orthogonal mapping matrix and a double-layer perceptron used for generating an article embedding mixed factor matrix are trained, so that the global article embedding matrix and the article preference embedding vector of a user are fused; a mixed article embedding matrix which is more personalized and finer in granularity is constructed for the client, and the mixed article embedding matrix is input into a personalized recommendation prediction perceptron constructed for the client to obtain a recommendation result, so that the final recommendation effect is improved; when the client side uploads data to the central server, local differential privacy encryption is carried out; according to the technical scheme, negative sampling is carried out on each client side, the positive samples and the negative samples are mixed and then jointly used for training, the training effect is improved, meanwhile, the server cannot distinguish the historical interaction situation between the client sides and the articles according to the update data uploaded by the client sides, and user privacy is protected.

The invention discloses an expert matching method and system based on large language model knowledge extension and fusion. The method comprises the following steps: firstly, collecting expert basic information, and enhancing through a pre-trained big language model to obtain expert extension information; and then, integrating the basic information and the extended information, and analyzing and fusing the basic information and the extended information by using RoBerta and a knowledge graph pre-training model to form expert fusion representation. According to enterprise technical requirements, requirement details are expanded through a large language model, and technical requirement fusion representation is generated in combination with representation analysis of the technical requirements and enterprise information. And finally, carrying out keyword correlation screening on the experts, narrowing a matching range, comparing fusion representation of the experts and requirements, calculating similarity and sorting, and recommending related matching experts. Through the method and the device, skill matching analysis can be effectively performed on external expert information, and expert matching precision and recommendation effect are improved based on semantic analysis and expert matching of technical requirements.

The invention discloses a multi-modal robust recommendation method and system based on self-supervised learning, and the method comprises the steps: extracting the modal features of each modal of a user and an article from original data as initial embedded vectors, and constructing a user-article heterogeneous graph and an article-article isomerism graph under each modal; performing multi-modal feature coding on the initial embedded vector of each modal, and performing multi-modal feature fusion on the enhanced final embedded vector of each modal; and performing multi-modal feature alignment to extract a final user embedded vector # imgabs0 # and a final article embedded vector # imgabs1 #, calculating to obtain a final prediction score of the user on the articles, sorting the final prediction score, selecting specified first K articles as a recommendation list recommended to the user, and outputting the recommendation list. The invention aims to eliminate the influence of inherent noise in the multi-modal data and improve the recommendation effect of the whole multi-modal recommendation system.

The invention discloses a nursing equipment recommendation method and system based on user historical behaviors, and relates to the technical field of equipment recommendation. Behavior patterns of users are identified, the users are divided into different groups based on a clustering result, user behavior data similarity is described by using a semi-variance function to form a semi-variance model; after a corresponding vacancy filling model is selected based on semi-variance model features, the selected vacancy filling model is trained according to the clustered user behavior data so as to estimate behavior data missing in time or space, the weight of each feature data is distributed in combination with the feature data of the user behavior data and a prediction result of the vacancy filling model, and the weight of each feature data is calculated. And setting a recommendation rule by using a multi-factor weighting method, and generating a nursing equipment recommendation list based on the recommendation rule. The recommendation system can utilize known data points to estimate missing values, so that the problem of data discontinuity or missing is solved, the space-time gap of user behavior data is filled after user behaviors are predicted, and the recommendation effect and accuracy are effectively improved.

The invention relates to the technical field of user personalized content pushing, in particular to a user personalized content pushing system based on big data and a promotion method. The method comprises the following steps: collecting user behavior and preference data through multiple channels; dynamically constructing user portraits, and enriching the user portraits in combination with multi-dimensional data; based on user portraits and content features, a recommendation model is constructed by using a deep neural network, and user preferences are accurately predicted through multiple hidden layers; through a push model and a multi-objective optimization method, the probability of pushed content is calculated in combination with indexes such as a click rate and a conversion rate, the push frequency and content selection are optimized, and the correlation of the pushed content and the user satisfaction are ensured; by monitoring user feedback in real time and adjusting a pushing strategy, intelligent decision and personalized recommendation of content pushing are realized, and long-term accurate pushing and improvement of user experience are ensured. According to the method, the accurate user portrait is constructed through multi-data source collection and processing, so that personalized content pushing is realized.

The invention discloses an SKU intelligent classification and label generation method fused with a visual pre-training model. The method comprises the following steps: obtaining image data and text data of an SKU; extracting multi-modal features based on the image data and the text data; according to the multi-modal features, zero sample classification of the SKU is realized; generating an SKU label through an improved fractal condition variational auto-encoder based on a multi-modal feature; on the basis of newly added SKU data, continuous learning of the model is realized; carrying out compression and mobile terminal deployment on the model; through an innovative fractal attention encoder and a chaotic dynamics condition generation decoder, the classification accuracy and the label generation quality are remarkably superior to those of an existing method, the BLEU scores of 95.8% and 0.76 are achieved respectively, commodities can be classified and described more accurately, and the search and recommendation effects of a platform are improved; and due to the continuous learning ability and the dynamic category extension mechanism, the performance is excellent when emerging commodity categories are processed, and the recognition rate reaches up to 89.2%.

The invention provides a short video recommendation method and system based on real-time interest enhancement, and the method comprises the steps: dividing a user historical interaction sequence into a long-term dimension, a short-term dimension and a real-time dimension, and carefully designing different network structures to extract the interest features of each dimension; long-term interest preferences, short-term behavior changes and instant demands of users are effectively integrated through a Gate Recurrent Unit (GRU), interest changes of the users on different time scales are adapted, and the weights of real-time interests are strengthened, so that the real-time interests prominently respond to the current behaviors and demands of the users; experiments are carried out on a plurality of data sets, and compared with a baseline algorithm, the algorithm effectiveness is proved. The technical problem that accurate personalized recommendation is difficult to perform in combination with the current interest of the user is solved.

The invention discloses a time interval perception multi-modal sequence recommendation method in the technical field of recommendation systems, which comprises the following steps of: extracting text features and picture features of a modal in a project, embedding, and extracting time interval representation of an interaction sequence of a user on the modal; and adding the time interval representation to the modal representation to obtain a time interval perception modal representation, establishing a user representation obtained by the user based on modal learning, aggregating the preference scores of the user on each modal to obtain an overall preference score, and performing recommendation according to the overall preference score. According to the method, a time interval coding method and a flexible two-stage fusion strategy are introduced, so that richer user preference representation is provided for a multi-modal recommendation system, and the recommendation performance is improved to a great extent.

The invention discloses a long-tail recommendation algorithm based on semantic enhancement and mixed attention. The algorithm comprises the following steps: fusing a user-article interaction graph and a knowledge graph to obtain a collaborative knowledge graph; on the collaborative knowledge graph, extracting new long-tail neighborhoods for the user and the article respectively by utilizing rich semantic information; long-tail proximity is gathered to the user and the article through relation attention, and long-tail embedding of the user and the article is obtained; further aggregating the obtained long tail embedding of the user and the article and the original proximity of the user and the article through self-attention to obtain a final embedding vector of the user and the article; and performing dot product on the final embedded vectors of the user and the article to obtain a prediction score of the user on the article, and recommending the first N articles with the highest prediction score to the user. According to the method, the long-tail recommendation performance can be improved on the basis of maintaining the recommendation precision.

The invention discloses an IPTV (Internet Protocol Television) service method. The method mainly comprises the following steps of: 1, constructing a recommended and matched effective item set by utilizing behavior data of an IPTV user; step 2, implicit feedback mining of the user in the IPTV is carried out based on the constructed recommendation matching effective item set; step 3, using the watching duration of the user and the total duration of the video in the implicit relation to carry out importance ranking calculation on implicit factors which influence the recommendation of the video program in the IPTV system; 4, according to the mined implicit feedback data of the user, calculating the selection rate of the user for the interested video program types; and step 5, on the basis of the existing historical watching behavior data of the user, according to behavior records of users with similar interests, expanding and recommending video programs which are never watched by the user but meet implicit requirements of the user. The method has the characteristics of improving the recommendation accuracy of a system containing individual preferences and improving the payment conversion rate brought by implicit demands.

The invention discloses a short comment generation method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring multiple pieces of comment information of first multimedia content; extracting a plurality of pieces of target comment information from the plurality of pieces of comment information, wherein the target comment information refers to comment information with recommendation significance for the first multimedia content; segmenting the plurality of pieces of target comment information to obtain a plurality of segmented short sentences; generating a short comment recommendation library of the first multimedia content through a language model according to the information of the first multimedia content and the plurality of segmented short sentences, the short comment recommendation library including a plurality of recommended short comments; non-unique short reviews in the multiple recommended short reviews are removed, a final recommended short review of the first multimedia content is selected from the remaining recommended short reviews, and the non-unique short reviews refer to recommended short reviews associated with multiple other multimedia contents except the first multimedia content. According to the method, the quality of the final recommendation short comments and the recommendation effect of the final recommendation short comments are improved.

The invention discloses a text representation alignment sequence recommendation method based on decoupling contrast learning, and belongs to the technical field of sequence recommendation and deep learning. The method comprises the following specific steps: constructing a text-based description data set for sequence recommendation; creating text semantic encoders of the articles and the sequences, and respectively generating corresponding text semantic vector representations; through unsupervised and supervised comparison of learning tasks, learning decoupling representation of sequences and target objects, and realizing bidirectional semantic alignment between the learning tasks and the target objects; according to the method, the overall semantics of the sequence and the features of the single object are effectively separated by decoupling the representation of the sequence and the target object, and the high-quality semantic representation of the interaction sequence and the target object is realized, so that the accuracy and effect of sequence recommendation are remarkably improved. According to the method, decoupling contrast learning is innovatively applied to sequence recommendation, the method can be widely applied to scenes such as e-commerce recommendation and content recommendation, and the method has important practical application value.

The invention relates to an intelligent product recommendation method and system based on a large model. The method comprises the steps of obtaining user information of a user to be recommended and product information of a product to be recommended; extracting user semantic features, user behavior features and natural language understanding features of the user information and product description features and product category features of the product information by using the large model, and performing feature fusion to obtain fused features; and obtaining a target recommendation score of each product relative to the to-be-recommended user, and recommending the product corresponding to the target recommendation score higher than a preset score to the to-be-recommended user as a target product. According to the method, the Transform model, the GNN model and the LLM model are combined, analysis and feature fusion of the user information and the product information are achieved, the extraction precision of the user semantic features and the product description features is improved, the user behavior features and the product category features can be effectively captured, and therefore the recommendation accuracy and the personalized level are improved.

The invention discloses a similarity and popularity combined collaborative filtering method. The method comprises popularity calculation and popularity and similarity combined collaborative recommendation. The invention provides an article popularity iterative calculation method introducing high-order information, and constructs a contrast loss function to realize effective fusion of similarity and popularity. Experimental studies performed on three common data sets show that compared with a mainstream collaborative recommendation method, the model provided by the invention has obvious advantages in the aspects of recommendation performance and operation efficiency.

The invention relates to the field of artificial intelligence, and provides an information recommendation method and device, equipment and a storage medium. The method comprises the steps that a recommendation model conducts recommendation estimation on multiple pieces of candidate recommendation information obtained through retrieval, target recommendation information recommended to a target object is determined based on corresponding information recommendation estimation values, and the recommendation model is obtained by adopting a loop iteration mode and based on first training samples of classification targets with the positive and negative sample number greatly different. In each round of iterative training, each first training sample is divided into a common sample or a difficult sample, a first weight value with a relatively low weight value is given to a target weight of the common sample, a second weight value with a relatively high weight value is given to a target weight of the difficult sample, and the second weight value is in positive correlation with the learning difficulty of the corresponding difficult sample; based on the first recommendation estimated value of each training sample and the target weight, model parameters are adjusted, and a model capable of correctly distinguishing positive and negative sample interfaces is adopted, so that the recommendation accuracy is improved.

The invention relates to a shopping center shop recommendation method and device, equipment and a medium, and the method comprises the steps: predicting interested shops of a target old user in a current time period through employing a long and short-term memory network model trained by user behavior time sequence data, adopting a preset collaborative filtering algorithm to calculate and determine a second similarity between the target old user and other users according to the interested shops of the target old user in the current time period, so as to determine neighbor users of the target old user; predicting an estimated score value of a target old user for an unscored shop according to the score value of the neighbor user for each shop, and calculating and determining a second recommendation degree corresponding to each shop according to the estimated score value, the actual distance between the target old user and the shop and the comprehensive recommendation value, and displaying each shop to a user interface of the target old user according to the second recommendation degree from high to low so as to complete shop recommendation of the shopping center. According to the invention, the traffic and user stickiness of a shopping center can be improved.

The application provides a dynamic increment-based graph information gate aggregation knowledge graph recommendation method, solves the data instability caused by random sampling and the noise fusion problem caused by double diffusion in the knowledge graph convolution recommendation method. The method comprises: dynamically selecting Top-K adjacent edges and adjacent point fusion for the current node in the knowledge graph (K is a positive integer of 10-20) in sequence. On the one hand, the matching score value of the current node, user embedding, adjacent edge and adjacent point is calculated by using the attention mechanism, the high correlation of the adjacent edge, adjacent point graph information and the candidate node of the next step is selected by using the score value, on the other hand, the selected graph information is adaptively adjusted and fused by using the gate mechanism, the adjacent information is accurately integrated into the current node embedding, and then the candidate node is diffused in sequence. Finally, through the operation of the adjacent fusion, the current node information and the adjacent graph information can be directly fused once, so that the noise information in the double diffusion and multiple fusion graphs is prevented.

The invention relates to the technical field of private domain traffic, in particular to a private domain traffic guiding method and system based on collaborative filtering, and the method comprises the following steps: S1, collecting user behavior data: collecting the behavior data of a user in a private domain platform in real time through client equipment; s2, constructing a user portrait: extracting the interest preference of the user, generating a user interest label, and forming the user portrait; s3, interest extension and cross-domain recommendation: constructing a relation graph between the user and the content, identifying association between the user interest node and the cross-domain content, calculating the edge weight between the user and the content node, and extending the interest boundary of the user in the private domain traffic; and S4, recommended content pushing: pushing cross-domain recommended content to the user, and meanwhile, optimizing the display sequence of the content in combination with the situation information of the user. According to the invention, the diversity and personalization of the recommended content can be enhanced, and the user experience and the diversity of the recommendation effect are further improved.

The invention relates to an object recommendation interaction method and device, electronic equipment and a storage medium. The method comprises the steps that recommended media content corresponding to a target object is displayed on a preset page; in the recommended media content display process, displaying a first part of picture in a first preset recommended picture corresponding to the target object in a preset area in a preset page, and displaying a second part of picture in the first preset recommended picture in the preset page; in response to a preset interaction operation triggered by the target account, moving the second part of the picture to a first preset sub-region in the preset region to enable the preset region to display a complete first preset recommended picture; and skipping to an associated page corresponding to the target object under the condition that the complete first preset recommendation picture is displayed. By utilizing the embodiment of the invention, the presentation style of the object recommendation picture can be enriched, the effectiveness and interestingness of user interaction are improved, the user interaction experience is improved, and the object recommendation effect and the object recommendation efficiency are improved.

The invention provides a personalized recommendation method for multi-behavior association modeling and interest boundary perception. Comprising a transformer-based multi-behavior sequence modeling module, a time-aware graph aggregation module and a user interest boundary prediction module, and a complete system is realized through training. An interaction sequence generated by a user before the current moment and a corresponding behavior type are embedded and then pass through the transformer-based multi-behavior sequence modeling module to obtain a behavior and sequence sensing node representation, and then through the time sensing graph aggregation module, a representation of a one-hop center node is obtained through aggregation according to the distance of a time interval. Stacking L layers to expand the user or project interaction diagram to L hops to obtain user node and project node representations; and finally, performing loss function optimization through the user interest boundary prediction module after the user node and the project node representation are subjected to MLP to obtain a prediction result of the user interest, and realizing personalized recommendation.

The invention relates to a movie recommendation method based on a social network and collaborative filtering, and belongs to the technical field of recommendation systems. The method comprises the following steps: downloading a Douban data set, carrying out data processing, and constructing a user-score matrix according to air.index data in the data set; a user-movie bipartite network is constructed; constructing a user explicit social network according to the social.index data in the data set; performing single-mode projection on the user-movie bipartite network to obtain an implicit social network of the user; the explicit social network calculates the explicit similarity of the user, calculates the implicit similarity of the user according to the implicit social network of the user, and obtains the fusion similarity of the user; a target user is given, top-n users with the highest similarity with the target user are obtained, and a recommendation list is calculated according to scores of the similar users. According to the method, the two social networks of the user relationship are fused, the collaborative filtering algorithm is combined, the problems of user cold start and data sparsity in a recommendation system are solved, and the recommendation diversity is enhanced.

The application discloses a kind of individual and group-oriented behavior internet modeling and prediction recommendation method, the method includes the following steps: step 1: establish the behavior preference model of behavior internet based on hypergraph;Step 2: extract the relationship between behaviors based on the model of step 1;Step 3: discover the behavior mode of user based on the behavior relationship of step 2;Step 4: predict the behavior trend of user based on the behavior relationship of step 2 and the behavior mode of step 3;Step 5: personalized service recommendation is carried out for user based on step 2, step 3, step 4.The model of behavior internet is proposed in the application, and the personalized behavior internet is obtained by cause-effect inference and relationship discovery, and the recommendation is carried out by using the deep learning method based on external knowledge, which improves the recommendation efficiency and effect for individual and group, overcomes the shortcomings that the traditional recommendation method based on deep learning is difficult to fully utilize personalized knowledge, and realizes the personalized and efficient recommendation of service.

The invention provides a text generation method and related equipment. The method comprises the following steps: performing text recognition on a sample video about a sample object to obtain sample information of the sample object and a corresponding initial text; performing abnormal text correction and text screening on the initial text to obtain a screened text; performing model training based on the sample information and the screened text to obtain a text generation model; the text generation model generates a target text used for recommending the target object based on the target information of the target object.

This application provides a video recommendation method, apparatus, device, and storage medium, relating to the field of communication technology. The method includes: upon receiving a video acquisition request, acquiring a candidate video list; acquiring the interaction probability between the user and each candidate video in the candidate video list, the interaction probability being determined based on the user's reconstructed embedding vector and the candidate video's reconstructed embedding vector; the user's reconstructed embedding vector being determined based on the user's embedding vector, the user's feature vector, and a pre-trained vector reconstruction model; the candidate video's reconstructed embedding vector being determined based on the candidate video's embedding vector, the candidate video's feature vector, and the vector reconstruction model; simulating the input of sample data during cold start when training the vector reconstruction model; and determining the target video to recommend to the user from the candidate video list based on the interaction probability between the user and each candidate video in the candidate video list. This method can accurately recommend videos to users during cold start, improving the video recommendation effect during cold start.

The invention is suitable for the field of artificial intelligence, and provides a transportation tool matching recommendation method and system based on a knowledge graph, and the method comprises the steps: converting a triple into a text form through employing an improved knowledge graph embedding model KGE, carrying out the coding of an input text through a BERT model, and obtaining the context representation of each Token; capturing global semantic information of the entities and the relationships based on the aggregation representation of the triple to obtain embedded representations of the entities and the relationships; integrating and extracting features of the heterogeneous information, and performing dimension reduction processing on feature data of the heterogeneous information by using principal component analysis (PCA) to obtain embedded representation of the heterogeneous information; the method comprises the following steps: performing feature dimension reduction on entities, relationships and heterogeneous information based on an embedded layer of a recommendation system algorithm NFM model, capturing a second-order interaction relationship between dimension reduction features through bilinear interaction operation, learning high-order feature interaction by using a feed-forward neural network FNN, outputting a matching probability of goods and a transportation tool, and obtaining a matching result of the goods and the transportation tool; and based on the matching probability, performing matching recommendation of the goods and the transportation tool.