Mixed recommendation method based on factorization condition limitation Boltzmann machine

Abstract

The invention provides a mixed recommendation method based on a factorization condition limitation Boltzmann machine. The method includes: constructing a target user feature vector; building a target user grading matrix set; building a target user training sample; building a factorization condition limitation Boltzmann machine model; grading and predicting to recommend results to a target user. The problems of user interest expression inaccuracy caused by pure recommendation based on content and data sparsity caused by recommendation based on behavior are effectively solved.

Description

technical field [0001] The invention relates to the field of information extraction, in particular to a hybrid recommendation method based on factorized conditionally restricted Boltzmann machines. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. At present, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find the information they are interested in from a large amount of information; It is also a very difficult thing for the information to stand out and attract the attention of the majority of users. Recommender systems are an important tool to resolve this contradiction. The task of the recommendation system is to associate users and information. On the one hand, it helps users find information that is valuable to them, and on the other ha...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problem that traditional recommendation methods and systems cannot effectively combine user behavior data and item content attributes, the present invention uses ite

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