关键词: 协同过滤, 兴趣链, 兴趣矩阵, 全局偏好, 相似度

Abstract: How to recommend the most interested information to users accurately from a large amount of disordered information has become an important research subject in service recommendation system. This paper propose a recommendation algorithm based on the combination of the user's interest matrix and the global preferences, which is used for personalized service recommendation. This paper firstly introduces the interest tag mechanism to form the user interest chain, and to form the user interest matrix by filling unevaluated service and complementing evaluated service on the user service rating set. Then, this paper calculates the partial similarity by the Euclidean distance of the user??s interest matrix. Lastly, this paper combines the global preference similarity based on the user cognitive difference and the global behavior difference. The algorithm can effectively integrate the user's preference information, also reduce the sparsity of the data set, and improve the recommendation accuracy. A large number of experiments on the real MovieLens 1M data set show that the proposed algorithm significantly improves the recommendation accuracy compared with the current representative recommendation algorithms.

Key words: collaborative filtering, interest chain, interest matrix, global preference, similarity