6results about How to "Solve the sparsity problem" patented technology

The invention belongs to the technical field of commodity recommendation, and discloses an e-commerce commodity recommendation optimization management system, which comprises the steps of collecting e-commerce comprehensive data, and preprocessing the collected e-commerce comprehensive data to generate a comprehensive data set; weight values are distributed for the user behavior characteristics through a comprehensive data set; in combination with the weights of the user behavior characteristics, scores of the users for all the commodities are calculated, and a user-commodity interaction matrix is constructed according to the scores; the festivals and the user positions are comprehensively considered, the preference degree of the users for the commodities is calculated through the user-commodity interaction matrix, and the optimal commodity category recommendation list for attracting the users is generated, so that the problems of improper regional difference processing and difficult recommendation system performance optimization are solved, the commodity recommendation accuracy is improved, the user experience is enhanced, and the user experience is improved. And the user experience and the operation efficiency of the e-commerce platform are improved.

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 discloses a facial expression recognition system and method based on a time sequence and vectorization, and the method comprises the steps: carrying out the processing of an inputted facial video sequence, extracting action units in each frame of image, and arranging the extracted action units into a time sequence according to a time sequence, the method comprises the following steps of: obtaining a plurality of action units, expressing all action units at each time point in a feature vector form, calculating the action units expressed in the feature vector form, generating a complete expression time sequence vector by using the calculated feature vectors, and identifying expressions by using a classification model based on the generated expression time sequence vector. According to the method, the time sequence and the vectorization technology are combined, the problems of sparsity and inaccurate recognition of action units are effectively solved, and the stability and the accuracy of expression recognition are remarkably improved through dynamic time sequence representation.

The invention relates to the technical field of automatic driving, in particular to an open set 3D occupancy prediction framework suitable for an automatic driving scene, which comprises a multi-source data processing module, an occupancy prediction processing module, a model training optimization module and a post-processing and visualization module. According to the open set 3D occupancy prediction framework suitable for the automatic driving scene and the implementation method, through a dynamic-static separation fusion-Poisson reconstruction technology, high-quality dense 3D occupancy representation is generated, holes are filled, semantic differences between a dynamic target and a static scene are reserved, the void rate of a voxel grid after densification is reduced in a nuScenes data set test, and the performance of the open set 3D occupancy prediction framework suitable for the automatic driving scene is improved. The method has the advantages that the accuracy of semantic tags is improved, the problem of sparsity of 3D data is solved, 2D visual features, 3D voxel features and CLIP text features are aligned through knowledge distillation loss, and high-precision prediction of known categories and effective recognition of unknown categories are realized at the same time through double prediction heads and an unknown category decision-making mechanism.

This application provides a source code generation method and apparatus. The method includes: determining target features based on a first fragment, wherein the first fragment includes one or more codes input by a user before a first moment, and the target features are used to indicate code attributes of the codes in the fragment; generating multiple candidate fragments; and selecting a second fragment from the multiple candidate fragments based on the target features, wherein the features of the second fragment have the highest similarity to the target features among the features of the multiple candidate fragments. The target features are obtained based on the source code input by the user and serve as the user's sequence preference. The source code suggestions provided to the user are generated based on the user's sequence preference. In other words, the generated source code is related to the source code input by the user. Therefore, the accuracy of the generated source code can be improved.

The invention discloses a diversity recommendation optimization method fusing inventory information. The diversity recommendation optimization method comprises the following steps: 1, constructing a data set of users and commodities; 2, constructing a matrix decomposition model; 3, constructing a utility maximization function; embedding commodity inventory constraints and commodity type constraints to optimize recommendation results; 4, a heuristic algorithm is adopted to solve the predicted score of the user for the commodity, and a final recommendation result is obtained. According to the method, the commodity recommendation model based on matrix decomposition is adopted, the inventory information of the commodities is fused, and the situation that the recommendation system recommends out-of-stock products is reduced, so that the recommendation fairness of the system and the satisfaction degree of the user to the platform can be improved.