Probability matrix decomposition recommendation method

Abstract

The invention provides a probability matrix decomposition recommendation method. The method provided by the invention is based on explicit and implicit feedback information and combined with a probability matrix decomposition technology at the same time so as to help a user to score the commodity. The method comprises the steps of firstly carrying out probability matrix decomposition on a user trust relationship matrix, then carrying out probability matrix decomposition on a commodity score matrix of the user and the implicit feedback information, solving an implicit user characteristic matrix, an implicit commodity characteristic matrix and an implicit feedback information characteristic vector by integrating results of the two times of decomposition, and finally calculating a recommendation score of the user. The method provided by the invention can help the user to recommend a score of the commodity by well utilizing an online social network, relieves a data sparse problem and a cold start problem in the social network, and has an excellent recommendation effect. Meanwhile, the probability matrix decomposition recommendation method can be applied to a recommendation system with a large-scale data set.

Description

technical field [0001] The invention relates to an interactive method of an online social network, establishes a new recommendation method, combines explicit and implicit feedback information, and uses the principle of probability matrix decomposition to perform accurate score prediction, and belongs to the application fields of software engineering, human-computer interaction, and Internet cross technology . Background technique [0002] In recent years, online social networks have become increasingly popular, attracting tens of thousands of users, and have become one of the main platforms for dissemination and sharing of information today. Due to the increasing scale of commodities, it is difficult for users to quickly and accurately find the commodities they are interested in. The task of the recommendation system is to help users quickly and accurately find the commodities they like. In online social networks, friend's recommendation is very important, it can provide us...

AI Technical Summary

Problems solved by technology

[0005] Technical problem: The purpose of the present invention is to provide a probability matrix decomposition recommendation method, which is a method based on explicit and implicit feedback information combined with probability matrix decomposition technology to help users rate products. The method is based on online social network As a platform, the probability matrix decomposition of explicit and implicit feedback information is performed, and the user's rating of the product is predicted based on Bayesian reasoning, which solves the problems of low prediction accuracy, sparse rating and cold start

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