Heterogeneous information network and Bayesian personalized sorting-based recommendation method and apparatus

Abstract

The invention discloses a heterogeneous information network and Bayesian personalized sorting-based recommendation method and apparatus. According to the method, a heterogeneous information network is used to describe a user, articles and related information, and an iterative method based on random walk with restart is used to calculate scores of the user to the articles. For weights of edges in the heterogeneous information network, a Bayesian personalized sorting-based machine learning method is used for automatic learning. In the learning process, an optimal solution is selected from a plurality of candidate solutions obtained by multi-round iteration through multi-time iterative solving. The invention furthermore discloses the heterogeneous information network and Bayesian personalized sorting-based recommendation apparatus. According to the recommendation method and apparatus, the weights of the edges in the heterogeneous information network can be automatically learned from data, the adaptability to different data is greatly improved, and user preferences can be better described, so that a better personalized recommendation result is obtained.

Description

technical field [0001] The invention belongs to the field of recommendation systems, in particular to a method and device based on heterogeneous information networks in the recommendation system. Background technique [0002] Recommended system: [0003] With the development of the Internet and mobile Internet, people are in an era of information explosion. In the face of massive amounts of information, how to efficiently filter out information that is interesting and helpful to users has become an increasingly important issue. Traditional search engines are increasingly unable to meet users' personalized information filtering and retrieval needs, especially when users cannot accurately describe their needs, the filtering effect of search engines will be greatly reduced. [0004] It is against this background that recommender systems emerge. Its task is to match users and items, so that users can find items that are valuable to them, and at the same time, items can be dis...

AI Technical Summary

Problems solved by technology

[0015] In order to solve the above-mentioned problem of poor adaptability to different data existing in the recommendation method based on heterogeneous information networks, the present invention provides a machine learning algorithm based on Bayesian personalized ranking, which can automatically learn heterogeneous Edge Weights in Structural Information Networks

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