25results about How to "Improve recommendation efficiency" patented technology

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.

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.

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.

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 application provides a label generation model training method, a label generation method, a device and equipment, and belongs to the technical field of Internet. The method comprises the following steps: acquiring a sample quadruple; encoding a sample video based on a label generation model to obtain a sample video feature, wherein the label generation model is used for generating a character label for a video based on the video, a character name of a character in the video and a face feature; predicting the sample video feature, the sample character name and the sample face feature based on the label generation model to obtain a prediction result; and training the label generation model based on the difference between the prediction result and sample supervision information. The above technical solution enables the trained label generation model to determine a label suitable for a video, and then enables the label to be used for video recommendation for a viewing object, so that the recommended video is more in line with the interest of the viewing object, the recommendation efficiency is improved, and the experience of the viewing object is improved.

The application discloses a kind of education resource sequence recommendation method, device and equipment based on three-view structure distillation and storage medium, the method includes: based on student-course interaction log, the learning sequence of time sequence course index is constructed, three types of view hypergraph are constructed according to semantic, path, concept structure multidimensional feature, three-view course representation matrix is generated by structure coding fusion, and the recommended list is output by inputting education resource recommendation model;Education resource recommendation model generates student state vector by the coding of Transformer, and the total course score is obtained by fusing three-view branch score and predicted;The present application predicts the education resource recommendation by three-view collaborative scoring from multiple perspectives, accurately captures course representation, effectively depicts learning path high-order law, improves the accuracy, explainability and generalization ability of education resource recommendation in sparse interaction and cold start scene.

This specification discloses a method, apparatus, device, and storage medium for enterprise recommendation. The method includes: obtaining an enterprise recommendation request from an enterprise, the enterprise recommendation request carrying enterprise information of the enterprise, the enterprise information representing the enterprise's products and its role in the industry chain, and the enterprise recommendation request requesting recommendations of partner enterprises to the enterprise; structuring the enterprise information to obtain product entities and enterprise role entities in the enterprise information; querying an industry graph based on the product entities to obtain multiple candidate partner enterprises, the industry graph including multiple enterprises belonging to different industry chains, the multiple candidate partner enterprises belonging to the same industry chain as the enterprise; and determining a target partner enterprise to recommend to the enterprise from the multiple candidate partner enterprises based on the enterprise role entities.

The embodiment of the invention provides a product recommendation method and device, electronic equipment and a storage medium, belongs to the technical field of artificial intelligence, and is suitable for the field of financial science and technology. The method comprises the steps of obtaining a target consultation text from a target object and original behavior data of the target object; performing user classification on the target object according to the original behavior data to obtain a target group type of the target object; screening out a target product type from preset original product types according to the target consultation text; selecting reference data from the target product type and the target consultation text according to the target group type, and performing product matching according to the target group type and the reference data to obtain a target product; and recommending the target product to the target object. According to the embodiment of the invention, the product recommendation efficiency can be improved.

This application provides a product recommendation method, apparatus, computer device, and storage medium. The product recommendation method includes: upon detecting a user's visit to an online store, obtaining the user's historical behavior sequence; generating product semantic identifiers based on the historical behavior sequence and a preset product hierarchical semantic library using a preset recommendation model; and determining at least one product related to the product semantic identifier from the online store based on the product semantic identifier, and recommending the at least one product to the user. This method can reduce computing power costs and improve recommendation efficiency.

This disclosure relates to a video recommendation method, apparatus, electronic device, storage medium, and program product. The method includes displaying video recommendation information corresponding to at least one first video on preset recommendation pages corresponding to multiple first videos, and previewing and playing a first target video on the preset recommendation pages based on a first preset magnification. The first preset magnification is greater than one, the first target video is any one of the multiple first videos, and the at least one first video is a video other than the first target video among the multiple first videos. Utilizing embodiments of this disclosure can improve the efficiency of user video browsing while simultaneously enhancing video recommendation efficiency, helping users quickly find videos of interest, effectively reducing the occurrence of accidentally clicking on videos of no interest, and thus effectively reducing unnecessary page jumps that waste system resources and improve system performance.

The embodiment of the invention provides a content recommendation method, a training method, a device, equipment, a medium and a program product, and relates to the field of content recommendation and the like, and application scenes include but not limited to red dot content recommendation scenes. The method comprises the following steps: acquiring object attribute information of a target object, content attribute information of a plurality of candidate recommendation contents and time information associated with the plurality of candidate recommendation contents; for each candidate recommendation content, determining a first probability and a second probability corresponding to the candidate recommendation content based on the object attribute information, the content attribute information of the candidate recommendation content and the time information associated with the candidate recommendation content; for each candidate recommendation content, based on a first probability and a second probability corresponding to the candidate recommendation content, determining a target recommendation probability for recommending the candidate recommendation content to a target object; and based on the target recommendation probabilities corresponding to the multiple candidate recommendation contents, determining target recommendation contents recommended to the target object from the multiple candidate recommendation contents.

The application belongs to the field of remote sensing algorithm process modeling and recommendation, and discloses a remote sensing algorithm online operation intelligent recommendation method for public prosecution of public interest litigation, acquires a certain number of remote sensing algorithm processes of environmental resource public interest litigation, and performs data cleaning and semantic classification on nodes; the standardized remote sensing algorithm process is matrixed to obtain a feature matrix; the feature matrix is subjected to an independent path extraction algorithm to obtain a plurality of independent path sequences; a prefix of the independent path sequence is subjected to frequent item mining to obtain a prefix part for training, and a tail node is used as a label; long-term features are extracted through a pyramid RNN model, and a random gradient descent algorithm is used to train a neural network to obtain a training model. The application simplifies the recommendation of remote sensing algorithms for environmental resource public interest litigation, converts a graph recommendation task into a sequence recommendation task, simplifies the knowledge demand of algorithm modeling personnel on a complex graph structure, and improves the recommendation efficiency of a recommendation system.

The application discloses a second-hand house source information retrieval and recommendation method and system, relates to the technical field of information retrieval and recommendation, and aims to solve the problem that users are trapped in repeated trial and error but cannot obtain a stable candidate house source set in the second-hand house source information retrieval and recommendation of the prior art. The method comprises the following steps: acquiring a retrieval demand input by a user, and constructing a composite demand state diagram; quantitatively evaluating the constraint strength of demand conditions corresponding to each node in the composite demand state diagram; and dividing the demand conditions into hard boundary conditions, preference boundary conditions and reference boundary conditions according to the evaluation results.

The invention provides a big data-based personnel qualification intelligent auditing and information filing management method, which comprises the following steps of: obtaining the type of a department to be managed, the number of departments corresponding to each type, posts of each department and information data of each personnel corresponding to each post, and establishing a department management database B; based on a department management database B, extracting information data of each person in a preset retirement age limit threshold e in real time, determining information data of each demission person, vacant post data formed by person transfer and newly added post data, and generating a missed post data set Q; the invention aims to solve the problems that the existing personnel management method is lack of dynamic association analysis capability between post missed allocation and personnel allocation, so that personnel allocation depends on manual judgment, the efficiency is low, proper personnel selection is easy to omit, and the management efficiency and decision scientificity are influenced.

This application discloses a method, apparatus, and computer-readable storage medium for recommending technical objects. Relating to the field of big data technology, the method includes: receiving a work order text from a target account; converting the work order text into a target semantic vector; calculating the similarity between the target semantic vector and the semantic vectors corresponding to N preset historical work order texts, determining the similarity between the N historical work order texts, where N is a positive integer; selecting historical work order texts with similarity exceeding a preset threshold as target historical work order texts; selecting technical objects corresponding to the target historical work order texts based on a preset collaboration graph network, determining the target objects, where the collaboration graph network represents the relationship between the N historical work order texts and the technical objects; and determining the recommended technical objects corresponding to the work order texts based on the target objects. This application solves the problem that recommendations based on past experience by technical personnel in related technologies are inefficient and inaccurate.

The application realizes a social network entity reasoning method based on hyperbolic geometry knowledge representation. First, the social network graph and the user to be recommended are acquired, the social network graph and the user to be recommended are input into the query representation calculation module, the query representation and the user entity representation are input into the entity reasoning module; in the query representation calculation module, the geometric vector representation of the query is calculated, the geometric vector representation of the query is input into the entity reasoning module, is converted into the final query representation, is used for distance calculation together with the entity representation, the distance between the query representation and the entity representation is obtained, and the topk user entities are taken out from the distance, and the final reasoning result entity is obtained. The scheme constructs a geometric vector representation framework on a hyperbolic space, learns from the idea of a Venn diagram to design corresponding logical operations such as and, intersection and supplement on a representation space. The final reasoning result is obtained through the distance between the geometric representation of the query and the entity representation.

A course recommendation method, device, equipment and medium are disclosed. The method comprises: acquiring actual interaction messages between a target user in real time; determining a current interest value of the target user according to the actual interaction messages and a user label of the target user; determining a corresponding target recommendation strategy according to a comparison result between the current interest value and a preset interest threshold value, and using the target recommendation strategy to recommend a course to the target user. The present application solves the problem that the course recommendation in the prior art is subjective and based on human experience, resulting in low recommendation pertinence, and achieves targeted course recommendation to the target user, improves the course recommendation efficiency, and enhances the user experience.

The invention relates to the technical field of wearable intelligent equipment, and further relates to a sound effect recommendation method and device, a computer readable storage medium and wearable intelligent equipment. The method comprises the following steps: determining a first vector according to acquired behavior feature data and environment feature data; determining a current scene and a first confidence coefficient according to the multi-source sensing data; determining a plurality of target sound effect templates according to the current scene, the first confidence and the scene priority matrix; and determining a target divergence according to the first vector, the current scene and the plurality of target sound effect templates, and determining a target sound effect according to the target divergence. According to the method, on the premise that scene adaptability and brand specifications are guaranteed, learning of user preferences can be achieved, so that the problem that the recommended sound effect is different from the expectation of the user is effectively avoided, meanwhile, mistaken recommendation caused by low-reliability preferences is restrained through a confidence coefficient mechanism, and sound effect selection better fitting individual habits can be provided for the user.

This invention provides an information recommendation method, apparatus, electronic device, and computer-readable storage medium based on artificial intelligence. The method includes: traversing historical interaction behavior data of a target object to determine a sequence of historical interaction information within the historical interaction behavior data; performing time-series-based one-way encoding on the vectors of the historical interaction information sequence to obtain a time-series interest vector sequence of the target object; performing two-way decoding on the time-series interest vector sequence to obtain a sequence vector of information to be recommended corresponding to each recommendation position; mapping multiple sequence vectors of information to be recommended to obtain information to be recommended corresponding to each recommendation position; and generating a recommendation list based on the information to be recommended corresponding to each recommendation position. This invention enables the parallel output of multiple pieces of information to be recommended, improving the efficiency of information recommendation.

The application discloses a longitudinal federated EBM commodity recommendation method with differential privacy protection, and comprises the following steps: 1, a master server uses a consumption feature data set to train an EBM recommendation model; 2, a slave server number is initialized; 3, the slave server uses the consumption feature data set to train the EBM recommendation model; and 4, whether the EBM recommendation model training is ended is determined according to the number of iterations. The application can effectively protect the user consumption feature information, thereby preventing the leakage of the user consumption feature information and providing effective commodity recommendation.

Embodiments of the present application disclose a traffic construction path recommendation method and device, electronic equipment and a storage medium, wherein the method comprises: obtaining a starting administrative region and a terminal administrative region of a traffic construction path; performing grid labeling processing on the starting administrative region and the terminal administrative region to obtain grid labeled administrative regions; determining a grid region transfer action from a current grid labeled administrative region to a next grid labeled administrative region according to an advantage function or a state probability transfer matrix of the grid labeled administrative regions; wherein the grid region transfer action is for avoiding a target avoidance grid region between the starting administrative region and the terminal administrative region; and constructing a recommended traffic construction path according to the grid region transfer actions of the grid labeled administrative regions. The technical solution of the embodiments of the present application can improve the recommendation efficiency, accuracy, intelligence and reliability of the traffic construction path.

The application discloses a kind of dynamic response user preference home product intelligent recommendation method.It includes the following steps: the home product style attribute is extracted as home style feature vector;User preference is obtained and characterized as the input of recommendation algorithm;Adopt double strategy combination mode, dynamically respond to the preference of user, set strategy start condition, calculate the degree of user between the coordination, the satisfaction of user to home product and the coordination degree between different categories of home product, and intelligently recommend home product for user.The application obtains user preference dynamically and analyzes the relationship between it and home product attribute, considers the style coordination problem between home products, intelligently recommends suitable home product for user, reduces the time and effort required for user to select home product independently.

The invention provides an image-text recommendation method and apparatus, and an electronic device. The image-text recommendation method provided by the invention comprises the steps of obtaining a browsed image-text sequence of a user; the browsed image-text sequence serves as a sequence token to be input into an image-text recommendation model obtained through pre-training, a preset number of future sequence tokens output by the image-text recommendation model at a time are obtained, and the image-text recommendation model is obtained through training in a multi-token prediction mode; and based on the preset number of future sequence tokens, determining a preset number of future browsing image-text sequences matched with the browsed image-text sequences and recommended for the user. By predicting a plurality of future browsing image-text sequences at one time, the exposure preference is obviously relieved, so that the robustness of the model in long sequence generation is improved; besides, a plurality of future browsed image-text sequences are generated in one-time reasoning, so that the recommendation generation delay and the calculation overhead are greatly reduced, and the recommendation accuracy and the recommendation processing efficiency of the browsed image-texts recommended in the future are improved.

The invention relates to a recommendation processing method and device, electronic equipment, a medium and program product quality, and the method comprises the steps that in the display process of target media content comprising a preset object, target interaction recommendation information corresponding to a target account is displayed on a display page of the target media content, and the target account is a user account of the preset object; the release account of the target media content is not the target account; the target interaction recommendation information is used for indicating to execute an interaction operation for the target account; and in response to a trigger operation based on the target interaction recommendation information, executing an interaction operation for the target account. By utilizing the technical scheme provided by the embodiment of the invention, the association between the media content and the account corresponding to the included object can be established, while account recommendation is realized, a browsing user of the media content can be helped to quickly find the account of the interested object in the media content, the convenience of interaction between the browsing user and the recommended account is improved, and the user experience is improved. And therefore, the account recommendation effect and recommendation efficiency can be improved.