2 results about "Rating matrix" patented technology

The application discloses a time-aware adaptive interest point recommendation method based on K-means clustering, which comprises the following steps: first, collecting and sorting check-in data sets, and converting to generate a user-time-location three-dimensional score matrix; second, extracting a two-dimensional check-in score matrix in each time slot, and generating a one-dimensional score vector of each time slot by using a data compression technology; based on the one-dimensional score vector, the K-means method is used to cluster the time slot; third, calculating the dynamic similarity of users in each time slot; based on the time clustering, the score method of the traditional user-based collaborative filtering algorithm is improved, so that the interest point prediction score can be adaptively generated according to the current recommendation time; a plurality of unvisited addresses ranking at the front at the current time are recommended to the user; fourth, the recommendation quality is evaluated by using a recommendation precision index, and the accuracy and effectiveness of the proposed technology are evaluated by comparing the recommendation precision of the technology proposed by the application with that of other classical recommendation systems.

The application discloses a kind of multimedia-oriented generative generalization cold start recommendation method, comprising:1. the interactive record of user and product is used to construct score matrix;2. input layer is constructed by one-hot encoding mode, and user, product is mapped to different embedding space by combining the multimedia features of product;3. the embedding of user, product is optimized by bayesian ranking loss function;4. construct generative neural network, including: prior neural network, encoder, decoder;5. construct uniformity enhanced conditional variational autoencoder, to make the latent space of original variational autoencoder more uniform and distinguishable;6. new product embedding generation method based on clustering obtains new product generative embedding;7. the degree of love of user to new product is predicted by the way of vector dot product.The application can make recommendations for new products using multimedia information of products, thereby alleviating the cold start problem in the recommendation system.