A New Similarity Method Used in Recommender Systems

Recommender systems are becoming increasingly popular with the evolution of the Internet,and collaborative filtering(CF) is one of the most important technologies in recommender systems.The performance of CF systems degrades with increasing number of customers and items.So,a new multiple-level user similarity is presented,which not only overcomes the difficulty of data sparsity,but also solves the similar but not same problem.The experimental results show that the presented algorithm can improve the performance of CF systems in both the recommendation quality and efficiency.