Weighted trust relationship-based probability matrix decomposing and recommending method

Abstract

The invention discloses a weighted trust relationship-based probability matrix decomposing and recommending method. The weighted trust relationship-based probability matrix decomposing and recommending method comprises the following steps: acquiring a user item score information matrix and an inter-user trust relationship score matrix and filling a trust information matrix with trust data; for theuser item score information matrix, calculating the similarity of a target user and each user according to a similarity calculation formula; fusing the acquired inter-user similarity into trust scoredata to acquire weighted trust score data; combining the acquired weighted trust score data with a probability matrix decomposing method to acquire a probability matrix decomposing and recommending model based on the weighted trust relationship; successfully predicting an item in which the target user is interested according to the acquired recommending model. The weighted trust relationship-based probability matrix decomposing and recommending method mainly aims at a social network site with score information and trust data and is mainly applied to an electronic commerce system; high-qualityand high-accuracy recommendation for the target user is formed effectively.

Description

technical field [0001] The invention relates to a probability matrix decomposition method, in particular to a probability matrix decomposition method based on a weighted trust relationship. Background technique [0002] With the rapid development of web2.0, the world has begun to move from information scarcity to information overload, and we have entered the era of big data. Some internationally renowned companies have the problem of information overload but cannot find effective information to meet user needs and create value. [0003] At present, recommendation systems have been widely used in the fields of movies, books, music, news, web pages, images, etc., and there are recommendation algorithms for each field. The two most serious problems faced by recommendation algorithms are cold start and data sparsity. Moreover, traditional recommendation systems ignore the social or trust relationship between users. But the reality is that in the real world, people always turn ...

AI Technical Summary

Problems solved by technology

But the reality is that in the real world, people always turn to trusted friends, so the choice is easily influenced by trusted friends

Therefore, traditional collaborative filtering recommender systems purely for user-item rating matrices do not provide real output

Moreover, the current recommendation algorithm is still immature, and it is also a research hotspot of many scholars.

Initially, the recommendation system was divided into a content-based recommendation system and a collaborative filtering-based recommendation system. Content-based recommendation only considers the characteristics of the user itself (hobbies, gender, etc.) and the characteristics of the item (category, price, etc.) so there is recommendation accuracy inaccurate question

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