A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences
Wang Weijun, Song Meiqing
School of Information Management, Central China Normal University, Wuhan 430079, China;
Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
[Objective] To solve the scalability problem and data sparsity problem of the collaborative filtering. [Methods]This paper proposes an algorithm of collaborative filtering personalized recommendation through directionally mining users' preferences. Introducing time as a variable, the algorithm excavates in two stages. The first stage is to find the project-based weak similar users, the second stage is to use users' relevance and attribute similarity so as to do directional excavation and form a collection of recommendation. [Results]Experimental results show that the time complexity of the new algorithm reduces a magnitude. Furthermore, the more sparser the data is, the greater leading advantage the recommendation accuracy has. [Limitations] The algorithm recommends deeply by analyzing the users' existed preferences, and it doesn't involve the users' preferences which haven't appeared. [Conclusions]This algorithm has a strong ability to adapt to data sparsity and enhances its scalability at the same time.
王伟军, 宋梅青. 一种面向用户偏好定向挖掘的协同过滤个性化推荐算法[J]. 现代图书情报技术, 2014, 30(6): 25-32.
Wang Weijun, Song Meiqing. A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences. New Technology of Library and Information Service, 2014, 30(6): 25-32.
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