113results about How to "Improve recommendation accuracy" patented technology

The invention discloses a privacy protection recommendation method in combination with federated learning and a knowledge graph. The method comprises the steps that 1, a server initializes and distributes a federated learning global model; the client uses a differential privacy edge camouflage algorithm to increase the confusion degree of the original data; step 2, the client loads original data to the federated learning local model for training to obtain a declined federated learning local model gradient; step 3, the client uses an induction order RDP privacy algorithm to carry out gradient noise addition on the descending federal learning local model and uploads the model to the server; 4, the server receives the federated learning local model gradient after noise adding, and updates and evaluates a federated learning global model; repeating the step 2 to the step 4 until the model converges or reaches a preset number of iterations, and obtaining an optimized federated learning global model; and 5, inputting user data into the optimized federal learning global model, outputting an interest probability value, and judging the potential preference of the user.

The invention discloses an intelligent route distribution system based on vehicle owner behavior prediction, and relates to the technical field of route distribution, and the distribution system comprises a data collection module which is used for obtaining the historical driving data of a vehicle owner; the behavior prediction module is used for analyzing the driving habit of the vehicle owner and predicting the behavior of the vehicle owner; the real-time traffic analysis module is used for acquiring real-time traffic data and analyzing real-time traffic conditions, weather information and road condition changes; the route optimization module is used for calculating an optimal route based on the prediction result and the real-time data and recommending the optimal route; and the user interaction module is used for displaying the recommended route and receiving user feedback and adjustment suggestions. According to the invention, by analyzing the historical driving data and driving habits of the vehicle owner, the system can provide route recommendation conforming to personal preferences, the user experience is improved, real-time traffic data and weather information are integrated, it is ensured that the recommended route adapts to the current traffic condition and road condition change, and a more accurate and efficient driving route can be provided.

The invention provides a federal learning-based recommendation method and device, a program product and electronic equipment, and relates to the technical field of computers. The method is applied to a distributed recommendation system, the distributed recommendation system comprises a central service device and a plurality of local service devices, and the method comprises the following steps: the central service device receives respective corresponding second data sets sent by the plurality of local service devices, the second data set is obtained by performing recall processing on data in the corresponding first data set by the local service equipment by using a recall model; the central service equipment performs preliminary screening on the obtained second data set to obtain a third data set corresponding to the local service equipment, and sends the third data set to the corresponding local service equipment; and the central service equipment performs clustering sorting processing on the obtained fourth data set according to the associated information of the to-be-recommended user to obtain target information recommended to the to-be-recommended user. The recommendation efficiency and accuracy of the recommendation system are improved.

The embodiment of the invention provides a ticket business recommendation method and system, a travel server and a program. The method comprises the following steps: acquiring a ticket business search request of a user; determining search results of recommended traffic tickets and replacing multiple candidate replacement search information of the traffic tickets; aiming at any candidate alternative search information, determining a search preference score of the user on the candidate alternative search information; according to the candidate alternative search information and any candidate alternative search information, on the basis of a plurality of ticket features related to a user ticket decision, scores of feature differences, corresponding to the recommended traffic tickets, of the candidate alternative search information in all ticket features are determined, and feature difference scores of the candidate alternative search information are obtained; according to the search preference score and the feature difference score of each item of candidate alternative search information, determining the score of each item of candidate alternative search information; and determining recommended alternative search information according to the scores of the candidate alternative search information. According to the embodiment of the invention, the recommendation accuracy of alternative traffic ticket recommendation can be improved.

The invention discloses a multi-behavior graph recommendation method based on comparative learning. The recommendation accuracy can be improved by fully utilizing auxiliary behaviors. The method belongs to the field of artificial intelligence, data mining and recommendation systems. The overall flow chart is as shown in figure 1; the method comprises the following steps: firstly, generating different behavior embedding of a user and a project by using a LightGCN model, and synthesizing the embedding of each behavior type through a self-attention mechanism; and then performing three comparative learning tasks, namely behavior consistency comparative learning, behavior difference comparative learning and data enhancement denoising comparative learning. And finally, combining the comparative learning loss with the BPR loss of the target behavior, and optimizing the model to improve the recommendation performance. Compared with the prior art, the method has the advantages that the auxiliary behaviors are fully utilized, the difference between the behaviors is not concerned, the generality of the user reflected between the behaviors is more concerned, meanwhile, the influence caused by noise interaction can be effectively removed, and the method has wide application prospects in recommendation systems such as e-commerce and the like.

The invention provides a recommended verbal skill generation method and device and electronic equipment. The method comprises the steps of obtaining historical session data, classifying the historical session data to obtain a plurality of category identifiers, the historical session data being historical sessions between recommendation personnel and a user, and the category identifiers representing categories corresponding to contents of the historical session data; a large language model is obtained, a recommendation verbal skill corresponding to each category identifier is generated by adopting the large language model, the large language model is obtained through training of multiple groups of historical session data, and the recommendation verbal skill is the verbal skill used by a recommender to recommend products to the user; and performing feature classification on the user according to the historical session data to obtain a feature category, matching a recommended verbal skill corresponding to the feature category, and performing recommendation to the user by adopting the recommended verbal skill. Through the method and the device, the problem that the recommendation verbal skill cannot be adjusted according to the characteristics of the user when the product is recommended to the client in the prior art is solved.

The invention belongs to the technical field of deep learning and recommendation, and particularly relates to a recommendation method and system based on variational graph auto-encoder denoising, which are used for improving the performance of a recommendation system. The method comprises the following steps: constructing a user-project interaction graph and a knowledge graph; graph embedding initialization is carried out on project nodes in the knowledge graph, and embedding representation of the project nodes is generated; the method comprises the following steps of: enhancing a project of a knowledge graph by using a variational graph auto-encoder to obtain a mean vector and a variance vector of the project, then generating two potential representations of the project through two times of sampling, and reconstructing through a decoder to obtain two knowledge graphs; performing graph contrast learning in the interactive graph, optimizing representations of the user and the item; updating all to-be-learned parameters in the model according to the optimization target; and according to the final embedded representation of the user and the project obtained through optimization, calculating a predicted interaction probability of the user and the project and generating a recommended project.

The invention relates to the technical field of data processing, and discloses a fund product recommendation method and a computer readable storage medium. After current market factor exposure, current scale factor exposure, current value factor exposure, historical average market factor exposure, historical average scale factor exposure and historical average value factor exposure of the fund product are acquired, a flexibility score of the fund product is calculated and acquired in combination with a preset factor weight; calculating a scene adjustment factor based on the market emotion index of the current market cycle type, optimizing the initial feature weight combination, and calculating a comprehensive fund score of the fund product with the yield, the volatility and the flexibility score; and classifying fund products and investor risk preferences, obtaining comprehensive fund scores of all fund products of corresponding types based on the investor risk preferences, and selecting first k corresponding fund products from large to small as recommendation results.

The invention belongs to the technical field of computers, and provides a sightseeing recommendation method based on light scene perception of people, which comprises the following steps: acquiring a prediction time period corresponding to target sightseeing time and a light climate prediction type, and acquiring travel distance representation between scenic spots in a scenic area; obtaining the light scene experience evaluation of the scenic spot under the prediction time period and the light climate prediction type from the light scene experience relation table; the optimal touring route of the target touring time is determined in combination with the light scene experience evaluation of the scenic spots and the travel distance representation between the scenic spots, and the optimal touring route passes through different scenic spots and takes the entrance of the scenic spots as the starting point and the exit of the scenic spots as the ending point; recommending an optimal touring route to the tourist; the invention further provides a sightseeing recommendation device based on the light scene perception of the person. According to the invention, the optimal touring route can be guided by the perception of people on the scene, the recommendation accuracy can be improved, and the experience satisfaction degree of tourists can be improved.

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 the technical field of artificial intelligence, in particular to a client-based financial product recommendation method and device, computer equipment and a medium. According to the method, a configured reference model and a pre-trained local model are used to perform product recommendation on local sample user information, update loss is calculated according to an obtained reference recommendation product and a first prediction value thereof, and a local recommendation product and a second prediction value thereof, and the pre-trained local model is updated according to the update loss, so that the user experience is improved. Product recommendation is carried out on target user information by using the updated local model, and the local model is updated in combination with output of the reference model, so that the updated local model can retain generalization ability of the reference model and retain pertinence of the local model to local data, and therefore, data privacy security is ensured, and meanwhile, user experience is improved. The product recommendation accuracy of the client is improved, and then the financial product recommendation accuracy of financial or insurance business systems based on the local model is improved.

The present disclosure provides an object recommendation method, an object recommendation device, an electronic device and a storage medium, which can be applied to the technical field of big data. The object recommendation method comprises: determining M first objective indexes, N second objective indexes and a target variable relationship matched with an application scene, the target variable relationship being used to determine a to-be-recommended object, the first objective indexes being used to represent transaction characteristics of a user, the second objective indexes being used to represent social relationship characteristics of the user, M being greater than or equal to 1, and N being greater than or equal to 1; obtaining a recommendation data set of a target user, the recommendation data set comprising first objective data corresponding to the first objective indexes and second objective data corresponding to the second objective indexes; and determining the to-be-recommended object according to the M first objective data, the N second objective data and the target variable relationship.

The present invention discloses a collaborative filtering recommendation method and system based on trusted selection of time-sensitive neighbors, which predicts the score of a target user on a target project based on a score matrix, and the specific steps include: time-sensitive neighbor screening, trusted neighbor selection, and score prediction. The collaborative filtering recommendation method based on trusted selection of time-sensitive neighbors of the present invention fully considers two time-varying factors, abnormal changes in user interests and fluctuations in recommendation credibility, and sequentially utilizes three steps, namely, time-sensitive neighbor screening, trusted neighbor selection, and target user score prediction, to complete the recommendation to the target user, thereby alleviating the problems of low collaborative filtering recommendation accuracy, poor stability, and weak resistance to user profile injection attacks in the case of sparse data.

This application provides a recommendation processing method, apparatus, electronic device, computer-readable storage medium, and computer program product based on artificial intelligence. The method includes: acquiring the interaction features and object features of a target account; performing first attention processing on the interaction features based on a first object interest pool to obtain interaction interest features, and performing second attention processing on the object features based on a second object interest pool to obtain object interest features; acquiring the activity features of the target account, and performing selection processing on the interaction interest features and object interest features based on the activity features to obtain recall features of the target account; based on the recall features and the information features of the information to be recommended, obtaining target information from the information to be recommended, and performing a recommendation operation on the target account based on the target information. This application enables accurate personalized recommendations and improves recommendation processing efficiency.

The invention relates to the technical field of electric data processing, in particular to an interest point recommendation method, system and equipment based on spatio-temporal context awareness, and aims to solve the technical problem of low accuracy of a recommendation method in the prior art. The obtaining module is used for obtaining target user features containing social information; then, based on the target user, the friend user and the plurality of interest points, constructing a user interest point interaction bipartite graph for obtaining target user features containing interest point interaction preferences and a plurality of interest point interaction preference features; then, constructing an interest point space-time transfer diagram based on time and space information of accessing the plurality of interest points by the target user to obtain space-time preference characteristics of the plurality of interest points; and finally, performing probability mapping processing on the features to obtain recommendation values, and recommending the corresponding interest points before the sorting value of the recommendation values is smaller than a sorting threshold value to the target user, so that the recommendation accuracy is improved, and the user experience is improved.

The application provides a gift recommendation method, device and equipment and a storage medium; the method in the application comprises the following steps: receiving a gift recommendation request, extracting a target user corresponding to the gift recommendation request and user attribute characteristics of the target user; extracting historical user comments of the target user according to the user attribute characteristics, and performing sentiment tendency analysis on the historical user comments to obtain positive user comments; obtaining evaluation commodities corresponding to the positive user comments and target gift categories corresponding to the evaluation commodities, obtaining candidate gifts corresponding to the target gift categories in a preset gift database; obtaining historical gift comments of the candidate gifts, determining target gifts to be recommended according to the historical gift comments and the positive user comments, and recommending the target gifts to the target user. The target gifts that the target user is interested in are determined according to the historical user comments of the target user, and the recommendation accuracy of credit card gifts is improved.

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.

A movie recommendation method based on collaborative enhancement and graph attention neural network, comprising: first, calculating the respective collaborative movie neighbor sets of users and movies, and obtaining corresponding collaborative interaction embedding vectors; then, mapping the entities in the sets to a movie knowledge graph for propagation to obtain corresponding layer knowledge graph embedding vectors and multi-hop knowledge graph embedding vectors; finally, combining the movie knowledge graph embedding vectors and the collaborative interaction embedding vectors to obtain final user embedding vectors and movie embedding vectors, and calculating the predicted click probability of the user on the movie to obtain the predicted ranking result of the movie recommended to the user from high to low according to the probability. The present application considers the sufficient combination of collaborative information and knowledge graph information, has high accuracy and good recommendation effect.

The invention belongs to the field of big data, and relates to a material recommendation method, which comprises the following steps: acquiring session archive information between a financial management terminal and a client terminal, and extracting a keyword of the session archive information to obtain a session keyword of the session archive information; when a participation request sent by a client terminal is received, obtaining a target material putting strategy from a preset material putting library; matching the session keyword with a material keyword of the material information; if the matching is successful, generating a to-be-recommended material based on the material information, and determining delivery time based on session time and time conditions in the session archiving information; and sending the to-be-recommended material and the delivery time to the financial management terminal, so that the financial management terminal sends the to-be-recommended material to the client terminal based on the delivery time. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the session archiving information and the to-be-recommended material can be stored in a block chain. The recommendation accuracy of the to-be-recommended materials can be improved.

The invention relates to the technical field of information processing, and provides a resource recommendation method, a resource recommendation device, a medium, a product and electronic equipment. The resource recommendation method comprises the following steps: for each to-be-recommended resource, determining a first similarity between a first resource vector of the to-be-recommended resource and a first user vector of a candidate user; determining a target user corresponding to the to-be-recommended resource from the candidate users according to the first similarity; and recommending the to-be-recommended resources to a target client where the target user is located. According to the scheme, the unpopular resources can be effectively recommended, and the recommendation accuracy of the unpopular resources is improved.

The invention provides a financial product recommendation method and device, equipment and a storage medium, and the method comprises the steps: obtaining first user information; the first user information is input into a mixed recommendation model, first product recommendation information output by the mixed recommendation model is obtained, and the mixed recommendation model is obtained through training based on a collaborative filtering algorithm and a demographic recommendation algorithm; the first user information is input into a random forest algorithm recommendation model to obtain second product recommendation information output by the random forest algorithm recommendation model, and the random forest algorithm recommendation model is obtained based on random forest algorithm training; and based on the first product recommendation information and the second product recommendation information, determining a first financial management product recommended to the user. Through the method, the first financial product is determined by combining the results of different recommendation models, the advantages of different algorithms can be combined, the limitation caused by a single recommendation algorithm is avoided, and the recommendation accuracy of the financial product is improved.

The present invention belongs to the technical field of path optimization, and discloses an educational intelligent learning path recommendation system; including: establishing an efficient user learning willingness evaluation system and dynamically optimizing the learning path through multimodal data fusion. Collect the user's emotional text data and voice data, convert the voice into text and perform unified processing, including removing neutral vocabulary, punctuation marks and calculating the emotional score; establish a positive / negative vocabulary, combine the emotion intensity label, train a multi-layer perceptron model, predict the emotion intensity and calculate the willingness evaluation value in combination with the score. At the same time, the comprehensive willingness evaluation value is further calculated in combination with the user's screen switching frequency and heart rate mutation data. Finally, based on the evaluation value and stress status, dynamically recommend a learning path suitable for the user, and flexibly adjust the content difficulty according to the user status.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a mixed identifier generation type recommendation method and system based on local collaborative context, and the method comprises the steps: obtaining a user historical interaction sequence, and carrying out the multi-granularity clustering of users, and obtaining a group of each user; constructing a static identifier based on the article content, constructing a dynamic identifier in combination with the user group information and the local interaction sequence, and fusing to generate a mixed identifier of each article; constructing an instruction fine tuning task, embedding group information into an instruction prompt word, and learning by using a large language model to generate a mixed identifier of a next article from a historical sequence; and generating a recommendation result based on the optimized large language model according to the historical sequence of the target user and the group information thereof. According to the method, the local collaborative context is fused through multi-granularity group estimation, and the mixed article representation is constructed in combination with static and dynamic identifiers, so that the problems of single user interest modeling and semantic deficiency of article representation are effectively solved, and the accuracy and personalized level of generative recommendation are improved.

The present disclosure relates to a training method, information recommendation method and device for an information recommendation model. The information recommendation model training method includes: obtaining initial sample features and actual operation results of sample accounts for sample recommendation information; the initial sample features are obtained based on sample account features and sample information features; the initial sample features are input into the information recommendation model to be trained to obtain predicted operation results of the sample account for sample recommendation information; the model includes a gating structure and multiple feature extraction layers, the multiple feature extraction layers are used to output multiple target sample features, the output value of the gating structure represents the importance of each target sample feature, and the predicted operation result is obtained based on the multiple target sample features and the output value of the gating structure; based on the difference between the predicted operation result and the actual operation result, the information recommendation model is trained to obtain a trained information recommendation model. The present disclosure can improve the recommendation accuracy of the trained information recommendation model.

The invention discloses an electric power material commodity recommendation method and device based on an improved deep interest network. According to the method, the problem of inaccurate commodity recommendation caused by lack of sufficient interaction data of new articles or a long tail effect in an electric power material recommendation system is solved. By introducing an instance attention mechanism and a channel attention mechanism, feature expression is enhanced, so that a recommendation system can make accurate commodity recommendation under the condition of a small number of samples, and the method is particularly suitable for commodity recommendation scenes with professional characteristics such as electric power materials; and the smoothness of the training process is dynamically adjusted by using the Cauchy loss, so that the ability of the model to capture the deep characteristics of the high-dimensional incomplete data is enhanced, and the model is further endowed with stronger representation ability and robustness.

The present invention relates to the field of multimedia information processing technology, and in particular to a video recommendation method and device. The method comprises: obtaining a sample video rating set; wherein the sample video rating set is a rating data matrix R of the rating user for the sample video set; processing the sample video rating set to obtain a sample video hidden feature matrix A and a rating user hidden feature matrix B respectively; obtaining sample video display features and rating user display features respectively; processing the obtained sample video display features and rating user display features into a sample video display feature matrix C and a rating user display feature matrix D respectively according to the matrices A and B; processing the matrix C and matrix D according to a preset processing strategy and obtaining a matrix G; predicting the ratings of all videos according to the matrix G and recommending videos to target users according to the rating prediction results.

The present invention relates to a generative sequence recommendation model and construction method for recommendation fairness, the method comprising: injecting random noise into user interaction behavior to obtain a damaged interaction representation that obeys a Gaussian distribution, then designing a sequence denoising model in the reverse restoration process, and using a dynamic routing mechanism to mine multiple interest representations in user historical interaction behaviors, and performing two-stage training in combination with user-side modeling to complete denoising; imitating the forward noise addition process to make the project representation noisy and then use it to iteratively reversely reconstruct the generated project. Compared with the traditional sequence recommendation model, the present invention, on the one hand, constructs a generative recommendation paradigm based on a diffusion model, and models uncertainty and alleviates noise by learning the statistical laws of data and generating new data based on it; on the other hand, it avoids the influence of factors such as user sensitive characteristics that may lead to discrimination or unequal treatment on the recommendation effect, and further promotes the diversity of the generative sequence recommendation model by improving the fairness of the recommendation.

The application provides an information processing system. An information processing system, characterized by comprising: a processor; wherein the processor is configured to: provide an interface for receiving user-specific requirements to a user; analyze the user-specific requirements received from the interface, and generate prompt information for instructing a generative artificial intelligence model to perform subculture-related recommendations based on the specific requirements; and input the prompt information to the generative artificial intelligence model to enable the generative artificial intelligence model to generate a recommendation result.