Integrating Explicit Trust and Implicit Trust for Product Recommendation

Abstract

Recommender systems discovers users’ interests through users’ historical activities, and provides personalized recommendation for users. With the development of E-commerce, there are more and more users and items, which lead recommender systems to face a lot of challenges, such as data sparsity, cold start, scalability and so on. Adding trust information to recommender system provides a new way to solve the problem of data sparsity and cold start. There are two kinds of trust relationships between users. One is explicit trust, which can get from users’ trust list or friends list. The other is implicit trust, which can be obtained through users’ historical activities. In this paper, we propose a recommender system based on explicit trust and implicit trust. Each user’s predictive ratings consist of two parts, one is from the user’s explicit trust friends, and the other is from the user’s implicit trust friends. Experimental results on two datasets demonstrate that the proposed approach outperforms other state-of-the-art recommendation algorithms.