772 results about "Recommender system" patented technology

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention relates to the technical field of health management systems, and discloses a health management scheme recommendation system based on big data analysis. The system comprises a health data acquisition module, a health characteristic quantification module, a health state identification module, a health scheme prediction module and a health parameter coupling module. The health data acquisition module synchronously acquires three types of time sequence data of physiological indexes, health behaviors and environmental exposure of a user and performs timestamp alignment; a health feature quantification module extracts features from the aligned data and generates corresponding feature matrixes and vectors; the health state recognition module classifies the feature data according to a preset rule and generates a health state label set; the health scheme prediction module is combined with a health intervention measure knowledge base to generate an initial scheme set through an association rule mining algorithm; and the health parameter coupling module corrects the intervention intensity parameter of the initial scheme based on the mapping relationship between the human physiological response and the behavior intervention parameter. According to the system, multi-dimensional health data can be integrated, and an accurate personalized health management scheme is generated.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

A recommendation system is described, which identifies engaged fans for an artist and requests input from the engaged fans regarding a collaboration by the artist with at least one different artist. In implementations, engaged fans are identified as having user profiles on a media content platform that satisfy at least one threshold engagement criteria based on consumption of at least one media content item associated with the artist. The recommendation system presents a user interface that includes at least one prompt for feedback that enables engaged fans to recommend how the artist collaborate with others. In some implementations, the user interface includes controls that are selectable to define artist characteristics to feature in a collaboration and the recommendation system is configured to generate a synthesized collaboration by automatically combining different artists' characteristics using a trained machine learning model. Recommendations based on engaged fan feedback are then provided to artists.

The invention relates to the technical field of personalized learning, and discloses a personalized learning path recommendation system based on a knowledge graph. The system comprises a user portrait module, a knowledge graph construction module, a path generation module and an effect evaluation module. The user portrait module obtains user learning parameters of the learner; the knowledge graph construction module receives learning domain information, constructs a domain knowledge graph based on user learning parameters, sets knowledge node granularity, and performs association analysis on knowledge units to obtain association weight values of the knowledge nodes; the path generation module dynamically plans a learning path according to the association weight value and adjusts the learning path in real time in learning; and the effect evaluation module monitors the knowledge point mastering degree, the learning progress deviation and the path completion rate of the learner, and performs abnormal early warning. The system can provide learning paths fitting individual differences for learners, and improves the pertinence and flexibility of learning.

The invention discloses an operation and maintenance knowledge base analysis system and method based on deep learning, and is applied to unified operation and maintenance management of a hospital information system and medical equipment. The system collects operation and maintenance data such as operation logs, monitoring indexes, alarm events, work orders, maintenance records and the like, constructs a minute-level time window through equipment identification and technology triggering time, and merges multi-source heterogeneous data into operation and maintenance sub-events and operation and maintenance events. Log, index and text feature extraction is performed on each operation and maintenance sub-event, multi-modal feature fusion is performed to generate an operation and maintenance event feature vector, the operation and maintenance event feature vector is stored in a vector index database and is associated with an operation and maintenance knowledge graph, and similar historical event retrieval and root cause and processing scheme recommendation of a new operation and maintenance event are realized. In combination with the adoption and processing results of recommended schemes by operation and maintenance personnel, the system performs hierarchical management on the operation and maintenance knowledge and performs incremental updating on the feature modeling network, so that the operation and maintenance knowledge retrieval accuracy and the fault positioning efficiency are improved.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention discloses an intelligent personalized topic recommendation method based on a knowledge graph, and relates to the technical field of information retrieval, and the method comprises the steps: constructing a multi-dimensional subject knowledge graph which defines knowledge point entities and topic entities through the knowledge graph, and establishes structural relationships and capability dimension attributes between the entities; forming a structured knowledge basis for recommendation; collecting learning behavior data of the user, and generating a user knowledge state model for dynamically evaluating the knowledge state of the user in combination with the multi-dimensional subject knowledge graph; selecting a corresponding recommendation strategy according to an output result of the user knowledge state model, and performing multi-dimensional question matching based on capability dimension matching based on the multi-dimensional subject knowledge graph to generate a personalized question recommendation scheme; and dynamically optimizing the multi-dimensional subject knowledge graph, the recommendation strategy and the matching rule based on the feedback of the user to the recommendation scheme. The problems that a traditional recommendation system is shallow in knowledge association, rough in diagnosis and rigid in strategy are solved.

The embodiment of the invention provides a published content recommendation method and device, equipment, a medium and a product, and the scheme can comprise the following steps: filling a search keyword used for searching a target advertisement and candidate published content matched with the search keyword into a cue word template containing thinking chain guide information; and generating prompt information for being input into the large language model. Wherein the thinking chain guiding information is guiding information of a thinking chain of a recommendation reason generated for the candidate published content; the recommendation reason is a specific reason basis for putting the target advertisement by utilizing the candidate release content. And inputting the cue word information into a large language model to obtain recommendation information at least comprising the recommendation reason and a thinking chain corresponding to the recommendation reason. According to the scheme, the advertisement publisher can fully understand the recommendation basis of the recommendation system, the recognition degree and the satisfaction degree of the recommendation content are improved, and the overall user experience and the advertisement putting effect of the recommendation system can be improved.

The invention relates to the technical field of tourism resource information processing, in particular to a tourism hotel dynamic scoring and recommending method and system based on multi-factor fusion, and the method comprises the steps: constructing a dynamic game portrait containing a user space-time behavior mark and hotel multi-modal attributes, and carrying out the dynamic scoring and recommending of a hotel based on the dynamic game portrait; the method comprises the following steps: analyzing a recessive preference vector of a user and an adaptive strategy vector of a hotel by simulating an interactive game process of the user and hotel attributes, performing dynamic equalization matching on the recessive preference vector and the adaptive strategy vector to generate a hotel situation score, generating an anti-consensus recommendation sequence according to the hotel situation score, and performing recommendation on the hotel situation according to the anti-consensus recommendation sequence. The anti-consensus recommendation sequence is subjected to interpretable packaging, and a final recommendation result with a decision guide clue is output to the user, so that the user can obtain a personalized hotel recommendation list and can also understand logic and diversity values behind recommendation, and the user experience is improved. Therefore, the transparency, the user trust degree and the exploration satisfaction degree of the recommendation system are improved.

A system and method for facilitating training of large language model based recommender systems are provided. The system may utilize one or more LLMs to create probability distributions for binary classification tasks associated with specific user-item pairs. The probabilities may be utilized to rank one or more tasks directly. The training of the one or more LLMs may involve the use of Knowledge Distillation methods and may be based on incorporating a dual-label system such as, for example, hard labels and soft labels. The one or more LLMs training data may consist of user-item pairs and their corresponding features. The labels used in the training process may include binary classification labels and their respective probabilities. The system may further implement the trained one or more LLMs to determine rankings or recommendations associated with user engagement of one or more content items.

The invention discloses a recommendation system-oriented high-concealment poisoning attack detection method and application, and the method comprises the following steps: S1, user behavior and relationship modeling: constructing a user feature vector and symbiotic relationship graph, and describing user scoring behavior preference and a co-occurrence relationship; s2, importance pre-screening: based on similarity measurement and importance modeling of score distribution, filtering out normal users weakly related to potential attack users; s3, cross-graph relation decoupling: carrying out key relation extraction and dynamic and static relation separation on the user relation graph, and obtaining high-quality relation representation through a cross-graph fusion mechanism; and S4, double-hyper-sphere cooperative detection: normal user representation is restrained by using a concentric hyper-sphere shell, and abnormal user detection is realized through the degree of deviation from the boundary. According to the method, high-concealment poisoning attacks can be effectively detected in a real recommendation system environment, the detection accuracy is remarkably improved, the false alarm rate is reduced, and the method has good practicability and robustness.

The invention relates to the technical field of artificial intelligence and intelligent recommendation, and particularly discloses a personalized scenic spot recommendation method based on multi-modal knowledge graph comparative learning, which comprises the following steps: constructing a tourism field-oriented multi-modal knowledge graph, and then respectively establishing a visual feature embedding module and a text feature embedding module; then, modeling is carried out on a graph structure containing user interests through a knowledge perception graph convolutional network, user preferences, entity semantics and relation semantics information are fused, and structured knowledge embedding is obtained; introducing a structure-perceived comparative learning mechanism, and carrying out importance evaluation and enhanced disturbance on the graph structure; generating a unified multi-modal scenic spot representation through a gating fusion network; and outputting a personalized recommendation result. The multi-modal information is effectively fused under the conditions of weak supervision and data sparseness, high-precision scenic spot recommendation is realized, the personalization and robustness of a recommendation system are improved, and the method is suitable for application scenes such as intelligent travel, travel navigation and cultural scenic spot recommendation.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The invention, which relates to the technical field of education recommendation, discloses a student portrait-driven education agent recommendation system based on a cognitive diagnosis map, comprising a time-frequency decomposition processing module, a weight rhythm matching module, a time sequence coupling damping module, a dynamic difference readjustment module and a time-frequency domain self-balancing control module. And the time-frequency decomposition processing module is used for establishing a time-frequency decomposition processing layer based on drifting characteristics of student portrait parameters in a time dimension, and performing energy distribution analysis on multi-source dynamic data streams from learning behaviors, test performance and emotion feedback according to time slices. Through time-frequency decomposition and non-resonance rhythm matching, dynamic coordination of weight adjustment and student portrait drifting is realized, characteristic fluctuation amplification is prevented, and the stability of a learning path is guaranteed; and a dynamic closed loop is formed through time sequence coupling damping and time-frequency domain self-balancing control, stable convergence of cognitive features is realized, and the accuracy and continuity of educational agent recommendation are improved.

The invention discloses a short video content accurate recommendation system based on artificial intelligence image recognition, particularly relates to the technical field of short video personalized recommendation, and is used for solving the problem of matching of user interest and visual bearing capacity. The method comprises the following steps: extracting short video key frame spatial-temporal characteristics through a multi-scale convolutional neural network, constructing a cognitive load threshold curve in combination with a user micro gesture sequence, representing the tolerance range of a user to visual complexity in different time periods, and generating a visual complexity vector; calculating a visual load matching index by using an adaptive dynamic warping algorithm, and adjusting a candidate video sequence through a load penalty factor to generate an initial recommendation probability; modeling a user dynamic interest vector based on a gated loop unit network in combination with an attention mechanism; and finally, user interests and video semantics are fused through multi-dimensional features, a final recommendation list is generated through a multi-objective optimization algorithm under the constraint of visual load, and personalized recommendation of interest matching degree maximization and visual comfort optimization is realized.

The invention discloses an information recommendation method and device, a storage medium and electronic equipment, and relates to the technical field of recommendation systems.The method comprises the steps that in response to terminal interaction operation, input data is obtained; filling the input data into a first preset prompt word template according to a preset format to form first fusion data containing prompt guide information; inputting the first fusion data into a prediction model, and outputting an intention label and an associated extended keyword set through intention analysis and keyword extension processing; performing semantic matching and preference matching based on the intention tag, the extended keyword set and locally stored chat robot Chatt related data to obtain a candidate Chatt set; filling a second preset cue word template with the first fusion data and the candidate Chatpot set according to a preset format to form second fusion data containing an adaptive service scene; and inputting the second fusion data into the prediction model, and determining Chatpot recommendation information.

A three-stage pipeline is used to create a data structure for efficiently producing grounded recommendations that guarantee that the recommended items are part of a set, D. In the first stage, each item in D is converted into a vector representation. In the second stage, a hierarchical clustering method is used to build a tree based on the vector representations. Each item is a leaf node of the tree. Each non-leaf node represents a group of items or a group of groups of items, and so on. In the third stage, an LLM is used to generate text that encapsulates the information of the group (or groups) of items represented by each node. The generated tree is recursively traversed to generate recommendations.

The invention relates to the technical field of artificial intelligence, and discloses a system of a nutrition recipe recommendation system based on artificial intelligence in chronic disease intervention management and an application method. Comprising a data acquisition module, a data preprocessing and feature engineering module, a knowledge base management module, a personalized nutritional requirement modeling module, a recipe generation and optimization module, a user interface module and a model training and updating module which are in communication connection through a network. According to the method, personalized health data such as physiological indexes, chronic disease characteristics, diet preference, allergy and intolerance information of a user are collected in multiple dimensions, a nonlinear relation between the characteristics of the user and nutritional requirements is deeply mined in combination with a deep learning model, and an accurate personalized nutritional requirement model is generated according to specific illness conditions and body indexes of different chronic disease patients; therefore, recipe recommendation conforming to individual differences is provided, and the risk of aggravating the illness state due to improper diet is reduced.

The invention discloses an e-commerce advertisement accurate recommendation system based on deep learning, and relates to the technical field of e-commerce, and the system comprises the following modules: an entity index module used for constructing a node registry; the hypergraph construction module is used for continuously monitoring an event stream and constructing a dynamic hypergraph; the initial embedding module is used for extracting a node embedding tensor and a hyperedge embedding tensor; the graph representation learning module is used for extracting a final node embedding representation tensor and a final hyperedge embedding representation tensor by improving a GraphTransform model; the click rate estimation module is used for outputting the predicted click rate of each candidate advertisement; and the sorting decision module is used for screening a candidate advertisement list. According to the method, the limitation that a traditional recommendation method depends on simple characteristics and neglects high-order association and dynamic evolution information is overcome, and a powerful solution is provided for realizing efficient, accurate and personalized e-commerce advertisement recommendation.

The invention discloses a college scientific research topic selection recommendation system based on an academic literature map and an implementation method. According to the invention, through the interdisciplinary association mining module, academic resources in different disciplinary fields can be deeply integrated, and scientific researchers are helped to discover cross innovation points which are difficult to perceive in a traditional single-disciplinary perspective. By means of abundant entity relation networks in an academic literature map, the system can accurately capture potential association among keywords, technical methods and research requirements of different subjects, guide scientific researchers to find a new research direction from cross-domain cooperation, effectively break subject barriers and push frontier exploration of multidisciplinary fusion. Meanwhile, the research conclusion conflict detection module can sensitively recognize conclusion differences or conflicts for the same scientific problem in existing literatures, a breakthrough entry point is provided for scientific research topic selection, researchers are prevented from repeatedly inputting in the existing consensus field, and the originality and perspectiveness of research are improved.

The invention provides a postpartum physiotherapy instrument personalized treatment scheme recommendation system based on data driving, and belongs to the field of data processing. The method comprises the steps of obtaining postpartum complications contained in a to-be-recommended puerpera and a treated puerpera, index factors of each contained postpartum complication, a real-time data sequence in a physiological dimension, basic disease information in a plurality of dimensions and fixed values in a plurality of body dimensions, and obtaining a reference puerpera; and obtaining a physiotherapy instrument use scheme of the reference puerpera, and combining the distribution of the postpartum complication contained in each reference puerpera and the index factors of the same postpartum complication contained in the to-be-recommended puerpera and the reference puerpera to obtain the priority of each physiotherapy instrument used by the to-be-recommended puerpera. The objective of the invention is to solve the problem that when a physiotherapy instrument usage scheme is recommended to a to-be-recommended patient through a current FP-Growth algorithm, the body difference between different lying-in women is not considered, so that the obtained recommendation scheme is possibly not suitable for the to-be-recommended lying-in women.

The invention discloses a federal recommendation privacy protection method based on ring signature and selective aggregation, which realizes anonymization privacy protection and selective aggregation in a federal learning process through an innovative double-server architecture and a hierarchical security mechanism. The method comprises the following steps: 1) deploying an aggregation server and a security server in a system initialization stage, and completing parameter initialization and key distribution; 2) the client executes local model training, and anonymization privacy protection uploading is realized by adopting a ring signature technology; 3) the security server performs selective aggregation of credibility verification, and completes grouping and parameter fusion based on user similarity; and 4) ensuring the completeness and source credibility of model updating through a hierarchical security parameter distribution mechanism. While the recommendation precision is ensured, the user identity leakage and privacy tracking are effectively prevented, the comprehensive security guarantee is provided for the federal recommendation system, and the method has the advantages of low calculation overhead and high communication efficiency.

System and methods for predicting content items using a neural network model and performing reinforcement learning as continual learning for training the neural network model includes obtain a first dataset of user actions of a plurality of users at a plurality of user devices and a second dataset of historical data for the plurality of users, extract a first set of embeddings and a second set of embeddings from the first dataset and the second dataset, output a trained model based on applying the first and second set of embeddings, determine a set of candidate content items by the trained model, determine a prediction value for each respective candidate content item of the set of candidate content items by the trained model, and output one or more content items of the set of candidate content items based on the prediction value determined for each respective first candidate content item.

The invention relates to the technical field of recommendation systems and privacy protection, and provides a federal heterogeneous graph privacy protection recommendation system. Comprising a distributed HIN privacy protection and dynamic semantic recovery module, a perturbation graph comparison enhanced federated heterogeneous graph neural network training module and a model training privacy protection module. A two-stage perturbation algorithm is adopted, a user high-order mode in the shared HIN and user-project interaction data in the private HIN are protected respectively, and high-order semantic information is dynamically recovered in combination with a high-order semantic recovery network; user and project features are modeled through node-level and semantic-level attention mechanisms, meanwhile, the consistency of cross-perturbation views is enhanced through perturbation graph comparative learning, unsupervised semantic features are extracted, fine-grained preferences of users and projects are captured, and robustness to noise is improved. Finally, ranking loss and perturbation diagram comparison loss are adopted to jointly optimize embedding and model parameters. And the defects of the existing federal recommendation system are overcome.

The invention discloses a graph neural network link prediction method based on multi-dimensional similarity, and aims to improve the accuracy and generalization ability of missing link prediction in a complex network. The method is suitable for social networks, citation networks, recommendation systems and other actual scenes with isomerism, sparsity and dynamic evolution characteristics. In order to solve the problem that a traditional method only depends on local adjacency information or single topological similarity and is difficult to capture a high-order structure relation and multi-dimensional feature association, a unified measurement system fusing structure similarity, attribute similarity and path similarity is constructed, and potential association between nodes is deeply mined. By introducing a self-adaptive feature weighting mechanism, the model can dynamically adjust the multi-dimensional similarity fusion weight according to network features, and the expression and distinguishing capability of the node relationship is enhanced. On the basis, the deep representation learning advantage of the graph neural network is combined, a model structure with the selective feature fusion capability is designed, and precise modeling of a complex link generation mechanism is achieved. The method has good expandability and interpretability, and prediction deviation caused by network heterogeneity can be effectively relieved. Experimental results show that the method is obviously superior to the existing mainstream method on a plurality of real network data sets, and has high theoretical value and wide application prospect.

The invention relates to the technical field of intelligent label recommendation, and discloses a standardized label framework collaborative recommendation system based on artificial intelligence. The system comprises a multi-level professional label knowledge base, a large language model reasoning unit, a semantic consistency verification module, a path verification module, a collaborative decision module and a label recommendation engine. The multi-level professional label knowledge base stores a standardized label system containing a multi-level structure; after receiving the input content, the large language model reasoning unit generates an initial label set according to label system level constraint reasoning; a semantic consistency verification module compares the label and content semantic matching degree and outputs a result, and a path verification module verifies whether a label generation path accords with a hierarchical structure and gives a compliance indication; the collaborative decision-making module corrects the initial label set according to a preset rule when the semantic verification result conflicts or the confidence coefficient is insufficient; and integrating the corrected label set by the label recommendation engine, and outputting a final standardized label recommendation result to adapt to multi-field labeling requirements.

Conventional recommender systems lack context awareness and semantic reasoning capabilities, while language models lack the ability to clearly delineate recommendations from generated text so as to correct biases. Methods and devices for providing multi-modal fashion recommendations are described. In some implementations, a dataset is collected that includes conversations between users seeking fashion recommendations using natural language text and images. Recommendation items are identified in the conversations and mapped to known entities. The dataset is used to train a multi-modal machine learning model that can interpret visual semantics of an image to generate fashion recommendations in response to natural language requests. The known entities can be extracted from the model output and biases can be mitigated before the final recommendation is presented.

The invention relates to the technical field of e-commerce recommendation systems, and discloses a commodity recommendation system and method based on deep learning. The method comprises the steps of obtaining an interaction behavior sequence of a target user, performing framing and slicing to generate a behavior time period data frame set, and extracting multi-dimensional interaction features; entities and relationships are extracted from the commodity knowledge graph, the features are associated and matched with the entities, and a dynamic preference sub-graph is constructed; analyzing the sub-graph by using a multi-modal fusion neural network, and outputting an implicit intention vector; executing multi-hop reasoning in the knowledge graph based on the vector, and retrieving to generate a candidate commodity pool; and calculating potential association strength of the commodities and the users through cross-domain transfer learning, and performing sorting to generate a final recommendation list. According to the method, the short-term preference dynamic state of the user can be described in a fine-grained manner, and the implicit intention of the user is deeply mined through dynamic knowledge association and fusion analysis, so that the accuracy and individuation degree of a recommendation result are effectively improved.