Topic-level Trust in Recommender Systems
Fuguo Zhang, Shenghua Xu · 2007
Recommender systems have been widely used in helping people deal with information overload. In addition to traditional popular collaborative filtering recommender technology, recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy and robustness of recommendations. Previous work related to trust in recommender systems has focused on profile-level trust model. In this paper we argue that items belonging to different topics need different trustworthy users to make recommendation, so topic-level trust will be more effective than profile-level trust in incorporating into the recommendation process. Based on this idea, we design a topic-level trust model which helps a user to quantify the trustworthy degree on a specific topic, and propose a new recommender algorithm by incorporating the new model into the mechanics of a standard collaborative filtering recommender system. The results from experiments based on Movielens dataset show that the new method can improve the recommendation accuracy of recommender systems.