关键词: 协同过滤, 用户特征, K-means, Slope One, 最近邻搜索

Abstract: In terms of the problems that the collaborative filtering algorithm used in traditional user personalized recommendation has sparseness and lacks of scalability, this paper proposes a collaborative filtering algorithm based on user features clustering and Slope One filling. In the algorithm, firstly, the user attribute feature is used as the clustering basis, and the K-means clustering algorithm based on minimum spanning tree is used to cluster the users and generate K similar user sets. Secondly, based on the clustering analysis, the Slope One algorithm is used to predict and fill the user score matrix under the similar user sets. Finally, a hybrid cooperative filtering algorithm is used to perform the nearest neighbor search on the filled user score matrix to obtain the predicted score and produce the recommended result. The comparative experimental results show that the proposed algorithm can significantly improve the accuracy of the recommendation, effectively alleviate to the sparseness problem and have good expansibility.

Key words: collaborative filtering, user characteristics, K-means, Slope One, nearest neighbor search