Collaborative filtering recommendation algorithm based on trust propagation

Providing high quality recommendations for users is a significant topic in e-commerce environment, however,it suffers from data sparse problem. To address the problem, this paper proposes a collaborative filtering recommendation algorithm based on trust propagation. The algorithm proposes TSR weight combining trust, similarity and relationship to replace the similarity in traditional collaborative filtering algorithm in order to find neighbours. TSRCF algorithm solves the data sparse problem and helps users get high quality recommendations in the information overload environment. Experimental results based on Epinions data sets and Film Trust data sets demonstrate that the algorithm performs better than the traditional filtering algorithm in terms of accuracy.