A trust-enhanced collaborative filtering recommender system

Collaborative filtering(CF) strategy is widely used in recommender systems, but it also exists many weaknesses, for example:, chanages of preference and no user control of the system. In this paper, we propose a novel personalized strategy, which is used to deal with the weaknesses. On one part, a mechanism is introduced for user to manage his own trust relationship, which could increase user confidence for the system A Trust table is adopt for a single user to keep his own trust neighbors, trust degree can be changed or viewed. On another part trust value is used as a complementary factor to user similarity, which makes the recommendation more accurate, Experiment shows that the recommendation method has a better performance than traditional CF method, and it is believed to strengthen consumer confidence.