关键词: 协同过滤, 相似性, 个性化推荐

Abstract: To measure the similarity of the users is the core of collaborative filtering algorithm. The similarity of users has a significant impact on the results of personalized recommendation. In this paper, method of similarity measurement is optimized through analyzing users - item rating records and comparing with pearson and jaccard similarity. This optimization method is applied to empirical analysis of the data set provided by the MovieLens site. Empirical studies show that the new algorithm can improve the accuracy of personalized recommendation and overcome the impact of data sparsity on recommendation quality to some extent.

Key words: collaborative filtering, similarity, personalized recommendation