IMPROVED COLLABORATIVE FILTERING ALGORITHM VIA INFORMATION TRANSFORMATION

被引:44
作者
Liu, Jian-Guo [1 ,2 ,3 ,4 ,5 ]
Wang, Bing-Hong [1 ,2 ,3 ,4 ,5 ]
Guo, Qiang [6 ]
机构
[1] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China
[2] Univ Sci & Technol China, Ctr Nonlinear Sci, Hefei 230026, Peoples R China
[3] Shanghai Univ Sci & Technol, Res Ctr Complex Syst, Shanghai 200093, Peoples R China
[4] Shanghai Acad Syst Sci, Shanghai 200093, Peoples R China
[5] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland
[6] Dalian Nationalities Univ, Dalian 116600, Peoples R China
来源
INTERNATIONAL JOURNAL OF MODERN PHYSICS C | 2009年 / 20卷 / 02期
基金
中国国家自然科学基金;
关键词
Recommendation systems; bipartite network; collaborative filtering; RECOMMENDER SYSTEMS;
D O I
10.1142/S0129183109013613
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter beta to regulate the contributions of objects to user-user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity higher algorithmic accuracy.
引用
收藏
页码:285 / 293
页数:9
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