[Objective] In order to improve the quality of recommendation, rough set is introduced into collaborative filtering based on user clustering. [Methods] This paper proposes a collaborative filtering recommendation model based on rough user clustering. When off-line, it clusters all users by rough K-means user clustering algorithm, which assigns user to upper or lower approximation based on similarity and thus generates his initial neighbor. When on-line, the model starts searching the nearest neighbor from the target user's initial neighbor, forecasts his ratings and makes recommendation. [Results] Experimental results show that the proposed model decreases the Mean Absolute Error (MAE) about 14% when compared with traditional and item-based collaborative filtering, and decreases MAE about 10% when compared with collaborative filtering based on user clustering. [Limitations] When considering the importance of upper and lower approximation to adjusting the centroid of cluster, this paper ignores the impact of the number of user clusters and the threshold of the number of nearest neighbors. [Conclusions] This model can effectively improve recommendation accuracy, and has high feasibility and practical significance.
王晓耘, 钱璐, 黄时友. 基于粗糙用户聚类的协同过滤推荐模型[J]. 现代图书情报技术, 2015, 31(1): 45-51.
Wang Xiaoyun, Qian Lu, Huang Shiyou. Collaborative Filtering Recommendation Model Based on Rough User Clustering. New Technology of Library and Information Service, 2015, 31(1): 45-51.
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