A Hybrid Trust Degree Model in Social Network for Recommender System

Abstract

Recommender system is an effective way to help users to find the required information. In the social network, the recommendation is often from one user to another user. Therefore, it is necessary to determine how the two users trust each other. However, much work has paid more attention to the one-to-one trust relationship but ignored the many-to-one relationship. In this paper, we proposed a hybrid trust degree model to describe how two users trust each other. This model not only considers the direct trust degree and indirect trust degree between the two users, but also considers the group trust degree. The group trust degree describes how a user are trusted by other users in a group. The experiment result shows that hybrid trust degree can reasonably measure and calculate the credit between two users in a group.