Trust-based Collaborative Filtering

Collaborative Filtering is one of the most successful techniques of Recommender Systems. Despite its success, similarity-based Collaborative Filtering methods suffer from inherent weakness: users tend to rate few items. As a result, the similarity is not easily computed. This paper aims to solve the above problem by introducing the trust metric into Collaborative Filtering. We develop a novel computation model of trust by incorporating the tastes of users. Then we propagate trust throughout the trust relationship network, and more potential neighbors can be found. At last, we make recommendations based on trust-based Collaborative Filtering. Experimental results on a real extremely sparse dataset have shown best performance of our method in terms of MAE and Coverage when compared with similarity-based Collaborative Filtering methods.