Movie recommendation system and method based on improved deep structured semantic model

Abstract

A movie recommendation system based on an improved deep structured semantic model comprises a user behavior collecting and processing module, an offline training module and an online recall and sorting module, the offline training module receives merged data output by the user behavior collecting and processing module, and the online recall and sorting module takes out user feature vectors obtained by training of users according to attribute features of the users and recall movie subsets recommended for the users in a movie vector library by adopting an approximate nearest neighbor search technology; a movie recommendation method based on an improved deep structured semantic model comprises the steps of user behavior collection and processing, off-line training, online recall and sequencing and the like, movies conforming to user interests can be effectively mined according to dominant features and implicit interaction information of users and movies, personalized recommendation services are provided for the users, and the movie recommendation efficiency is improved. And obtaining a recommendation result of the user.

Description

technical field [0001] The invention relates to the field of semantic models, in particular to a movie recommendation system and method based on an improved deep structured semantic model. Background technique [0002] With the continuous development of the Internet, the size of the online video market has grown year by year. While enjoying the video feast with rich and varied forms and contents, Internet users are constantly being impacted by a large amount of redundant and invalid information. These huge amounts of data and information are far beyond what users can bear, and seriously interfere with the correct selection of the information they need, resulting in very low utilization of information, and even causing confusion and disgust to users. [0003] As an effective means to solve the problem of "information overload", the recommendation system has been developing rapidly in recent years, and its role in Internet services is also increasing. As a carrier of rich in...

AI Technical Summary

Problems solved by technology

The problem with this scheme is: when users have few rating records for movies, the collaborative filtering recommendation effect is very poor, that is, the cold start problem; as the number of users and movies continues to increase, the collaborative filtering matrix will be very large, and the use of the matrix The cost of decomposing the feature vectors of users and movies is getting bigger and bigger

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