An Improved Collaborative Filtering Recommendation Algorithm

Abstract

In order to improve the quality of recommender system, a new method of similarity calculation is proposed in this paper. Compared with traditional method, more factors like individual tendency and confidence level are considered which brings out higher similarity accuracy. Moreover, user attribute information is involved to solve the cold start and data sparseness problem. Finally, the proposed mothed is verified on experiments using Movie Lens dataset.