Recommender systems

被引:910
作者
Lu, Linyuan [1 ,2 ,3 ]
Medo, Matus [2 ]
Yeung, Chi Ho [2 ,4 ]
Zhang, Yi-Cheng [1 ,2 ]
Zhang, Zi-Ke [1 ,2 ,3 ]
Zhou, Tao [1 ,2 ,3 ,5 ]
机构
[1] Hangzhou Normal Univ, Inst Informat Econ, Alibaba Business Sch, Hangzhou 310036, Zhejiang, Peoples R China
[2] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland
[3] Univ Elect Sci & Technol China, Web Sci Ctr, Chengdu 610054, Peoples R China
[4] Aston Univ, Nonlinear & Complex Res Grp, Birmingham B4 7ET, W Midlands, England
[5] Beijing Computat Sci Res Ctr, Beijing 100084, Peoples R China
来源
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS | 2012年 / 519卷 / 01期
基金
中国国家自然科学基金;
关键词
Recommender systems; Information filtering; Networks; WORD-OF-MOUTH; EMPIRICAL-ANALYSIS; COMPLEX NETWORKS; LINK-PREDICTION; MATRIX COMPLETION; RANDOM-WALK; THE-ART; INFORMATION; TRUST; MODEL;
D O I
10.1016/j.physrep.2012.02.006
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 49
页数:49
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