Personal recommendation via modified collaborative filtering

被引：53

作者：

Liu, Run-Ran ^{[1
]}

Jia, Chun-Xiao ^{[1
]}

Zhou, Tao ^{[1
,2
]}

Sun, Duo ^{[1
]}

Wang, Bing-Hong ^{[1
,3
]}

机构：

[1] Univ Sci & Technol China, Dept Modern Phys & Nonlinear Sci Ctr, Hefei 230026, Anhui, Peoples R China

[2] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland

[3] Shanghai Acad Syst Sci, Inst Complex Adapt Syst, Shanghai, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2009年 / 388卷 / 04期

基金：

瑞士国家科学基金会; 中国国家自然科学基金;

关键词：

Recommendation system; Bipartite network; Similarity; Collaborative filtering; Infophysics;

D O I：

10.1016/j.physa.2008.10.010

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard cosine similarity, we take into account the influence of a node's degree. Substituting this new definition of similarity for the standard cosine similarity, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：462 / 468

页数：7

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