Social network friend recommendation method fusing trust degree

Abstract

The invention provides a social network friend recommendation method fusing a trust degree, and relates to user similarity, confidence factor calculation and fusion. The social network topology-basedrecommendation focuses on known friends and ignores potential interested friends; the interest-based recommendation focuses on recommendation of strange users, thereby difficultly getting trust of users; and both the social network topology-based recommendation and the interest-based recommendation do not consider behaviors of the users in a social network, thereby greatly influencing the accuracy, reliability and comprehensiveness of a recommendation result. The invention provides a recommendation method comprehensively considering social network topology, user interests and social behaviors.Firstly, social similarity is calculated out according to common neighbors in the social network of the users, interest similarity is calculated according to keywords, and linear combination is performed. The social topology and the social behaviors of the users are comprehensively considered, a relationship confidence degree and a behavior confidence degree are calculated out, and fusion is performed to form a confidence factor. Finally, the similarity and the confidence factor are fused, so that the trust degree of similarity calculation is improved, and a Top-N recommendation list is generated.

Description

technical field [0001] The invention relates to the fields of data mining and recommendation systems, in particular to a method for recommending friends in a social network that integrates trust degrees. Background technique [0002] With the development of Web2.0, social network has become a typical application in the development of Web2.0 model, and personalized recommendation technology has also been widely used in social network. Social network is social network service (SocialNetwork Service, SNS). Intuitively speaking, it can be said that it is a social network based on the network. Its development can be roughly divided into: E-mail, BBS, BLOG, Facebook / Renren and other stages. In social networks, users interact and share information through adding friends and following mechanisms, and adding friends to each other will inevitably create a connection between users. With the development of time, users and their friends and friends of friends Together, they form a socia...

AI Technical Summary

Problems solved by technology

The recommendation through social network topology focuses on recommending friends you know offline, and the similarity index is mainly obtained through mutual friends, while the recommendation through interest focuses on recommending unfamiliar users with the same interests, and the recommendation results are often difficult to gain the trust of users.

At the same time, neither of these two recommendations takes into account the user's behavior in the social network, which greatly affects the accuracy, reliability and comprehensiveness of the recommended results.

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