9results about How to "Improve recommendation quality" patented technology

The embodiment of the invention discloses an investment attraction information recommendation method, electronic equipment and a readable storage medium. The method comprises the steps that investment attraction party data and investor party data are collected; constructing an investment attracting party and investor knowledge graph, an investment attracting party attribute graph and an investor attribute graph; an investment attraction information recommendation model is constructed, the investment attraction information recommendation model comprises an input layer, a graph convolution layer and a prediction layer, and the input layer fuses internal interaction information and cross interaction information of an investment attraction party attribute graph and an investor attribute graph to obtain embedded representations of an investment attraction party and an investor; embedding representations of the investment attracting party and the investor are input into a graph convolution layer, the graph convolution layer obtains final representations of the investment attracting party and the investor through multi-layer aggregation operation, the final representations are input into a prediction layer, and the prediction layer obtains correlation scores of the investment attracting party and the investor through a three-layer multi-layer perceptron; and selecting a preset number of investors with the highest score to recommend to the investment attracting party.

The invention relates to the technical field of code completion, and discloses an intelligent code completion method under a programming education scene, which comprises the following steps: simulating answering codes of different styles of students through a general large language model in which programming learner identities are preset; each symbol of an answer code in the extended data set is coded, an obtained coding sequence is input into the large language model, the large language model predicts the next symbol of the coding sequence according to the previous part of the coding sequence, the prediction precision of the large language model is optimized by minimizing cross entropy loss, and a complementation model is obtained; and inputting answer codes of the students into the completion model which completes training to obtain an initial recommendation list, selecting three symbols which are most related to the scene information in the initial list, and increasing the order of the three symbols in the initial recommendation list to generate a final recommendation list. According to the method, the recommendation list is optimized by means of the scene information, and the recommendation quality under the programming education scene can be effectively improved.

The application provides a catering recommendation method and system applied to a digital multimedia online platform, relates to the technical field of the digital multimedia online platform, and first acquires user catering interaction intention data and platform internal catering content supply time sequence data; constructs a user catering intention evolution model based on the user catering interaction intention data and the platform internal catering content supply time sequence data, forms an intention evolution link; establishes intention supply time sequence adaptation logic in combination with the model and the supply time sequence data; screens content from a platform catering content library according to the adaptation logic to generate a dynamic recommendation scheme; pushes the dynamic recommendation scheme to a user interface and captures new interaction intention data, integrates the original data to update the model and the adaptation logic, so that the dynamic change of the user intention can be accurately grasped, dynamic recommendation is combined with supply time sequence characteristics, and the user consumption experience and platform competitiveness are improved.

This application discloses a content recommendation method and related products, relating to the field of artificial intelligence technology. The method involves: obtaining a first sequence of operation information of a target object on a specified type of historical content in the current terminal device; obtaining a second sequence of operation information of the target object on a specified type of historical content under the current account; obtaining a first attention enhancement vector for the first operation information sequence through a first attention mechanism; obtaining a second attention enhancement vector for the second operation information sequence through a second attention mechanism; fusing the first and second attention enhancement vectors to obtain an attention fusion vector; performing feature cross-processing on the first, second, and attention fusion vectors to obtain a preference vector; and determining target content to be recommended to the target object from a candidate content library based on the similarity between the candidate content vector and the preference vector. This application can improve the quality of content recommendation.

This invention discloses a method, apparatus, device, and storage medium for determining a course recommendation list. The method includes acquiring sample data; obtaining a target basic data dictionary based on the sample data; obtaining a target sparse vector based on the sample data and the target basic data dictionary; acquiring an initial feature vector of the object to be recommended; obtaining a target feature vector of the object to be recommended based on the target basic data dictionary, the target sparse vector, and the initial feature vector of the object to be recommended; and determining a course recommendation list for the object to be recommended based on the target basic data dictionary and the target feature vector. The technical solution of this invention provides a new method for determining a course recommendation list, fully considering the characteristic information of employees from various dimensions, effectively avoiding the scenario of only recommending courses of personal interest during subsequent course recommendations, and improving the quality and accuracy of course recommendations.

This invention provides a method and apparatus for training a recommended works determination model, relating to the field of data processing technology. The method includes: acquiring historical behavior data of sample users; extracting sample content feature information and sample cover image feature information to obtain sample user feature information; generating sample feature information based on the sample content feature information, sample cover image feature information, and sample user feature information; inputting the sample feature information into a recommended works determination model to obtain an evaluation score; calculating the sample loss of the recommended works determination model based on the evaluation score and the historical behavior data of the sample users; adjusting the parameters of the recommended works determination model based on the sample loss; and returning to the step of inputting the sample feature information into the recommended works determination model until a preset training convergence condition is reached, resulting in a trained recommended works determination model. The solution provided by this invention can be used to train a model capable of recommending works to users.

The invention discloses an archive data management system and method in a distributed environment, and relates to the technical field of data analysis. Paper archives are converted into electronic archives by scanning instruments, an electronic archive system is established, and after user registration and identity authentication, query conditions are input to generate query records, historical query records are obtained, recommendation parameters are collected and recommendation scores are calculated, abnormal recommendation records are determined, user permissions are assigned, user information in abnormal recommendation records is collected, user personal scores are calculated, user levels are determined, abnormal frequencies of levels are calculated, abnormal user levels are determined, training cycles are set, recommendation success rates of abnormal user levels in a certain training time period are calculated, characteristic time periods of abnormal user levels are determined, and real-time recommendation success rates in characteristic time periods of abnormal user levels are calculated. If the rate is lower than a recommendation success rate threshold, management personnel are reminded to optimize recommendation methods and improve recommendation accuracy.

The present application relates to natural language processing technology in the field of artificial intelligence, and discloses a prediction method and computer device for word semantics based on semantics. The method, based on HowNet, constructs a semantic space of words based on semantics, and represents known words in HowNet in the semantic space. The vector representation of words in the semantic space consists of two parts, one part of which is determined by the category semantics, and the other part of which is determined by the extended description and the relationship between the extended description and the category semantics. Since the meaning of the semantics is clear, the vector used in this application to represent words is interpretable; in the face of newly added words, the correlation between the newly added words and the known words can be calculated, and the vector representation of the newly added words can be derived from the vector representation of the known words in the semantic space. The newly added words predicted by this method have strong representation capabilities. The method of this application can be combined with a recommendation system to solve the problem of understanding and representing new words in the recommendation system and improve the quality of recommendations.

The invention relates to a privacy protection recommendation method and system based on matrix decomposition, and belongs to the technical field of privacy protection recommendation. Comprises: obtaining a historical score matrix of a user; performing matrix decomposition on the historical score matrix to obtain a shared score matrix; sending the shared scoring matrix and the scored item set to a recommendation server, and generating public parameters, a decomposition matrix and a decomposition matrix shared value; generating an inner product triple and a multiplication triple by using a triple server; calculating an error between the real score and the predicted score by using the recommendation server, and generating an error sharing matrix; the error sharing matrixes are optimized until the error sum of squares of all the error sharing matrixes is smaller than a threshold value; calculating a partial derivative of the loss function to the decomposition matrix to obtain a gradient sharing matrix; updating the decomposition matrix to obtain a user decomposition shared matrix; and generating a privacy protection recommendation result of the user by using the user decomposition sharing matrix. According to the invention, powerful guarantee is provided in the aspect of protecting user privacy.