Movie recommendation method and system fusing end-to-end training of visual features

Abstract

The invention discloses a movie recommendation method fusing end-to-end training of visual features. The method comprises the steps of receiving an input user score matrix, initializing a user featurematrix and a movie feature matrix, and then building an initial model in combination with a visual feature matrix; and performing the end-to-end training by utilizing the initial model to obtain a user score prediction matrix, and recommending movies for users according to the user score prediction matrix. According to the method, the learning of the visual features such as a poster, a key frameand the like, and a recommendation model are fused to a unified framework, and the end-to-end training is carried out; the learned visual features not only have relatively high expression and classification capabilities; the obtained user score prediction matrix can fully reflect the preferences of the users to the visual features; and the movies recommended for the users also can better conform to the user preferences. The invention further provides a movie recommendation system fusing the end-to-end training of the visual features, a server and a computer readable storage medium, which havethe abovementioned beneficial effects.

Description

technical field [0001] The present application relates to the field of recommendation, and in particular to a method, system, server and computer-readable storage medium for movie recommendation that integrates end-to-end training of visual features. Background technique [0002] With the continuous development of mobile Internet technology, the amount of information on the network is rapidly expanding and increasing exponentially, and the problems of information overload and information wandering on the network are becoming more and more serious. In order to provide users with satisfactory information and services, recommender systems emerged as the times require, and have become a research field that many researchers pay attention to. The recommendation system performs information filtering by predicting the user's preference for information resources, and performs personalized recommendations through collaborative filtering algorithms and other technologies according to t...

AI Technical Summary

Problems solved by technology

However, posters and keyframes are an integral part of the film, but are rarely used effectively

Images