Recommender systems survey

被引：1960

作者：

Bobadilla, J. ^{[1
]}

Ortega, F. ^{[1
]}

Hernando, A. ^{[1
]}

Gutierrez, A. ^{[1
]}

机构：

[1] Univ Politecn Madrid, Madrid 28031, Spain

来源：

KNOWLEDGE-BASED SYSTEMS | 2013年 / 46卷

关键词：

Recommender systems; Collaborative filtering; Similarity measures; Evaluation metrics; Prediction; Recommendation; Hybrid; Social; Internet of things; Cold-start; OF-THE-ART; SIMILARITY MEASURE; SOCIAL NETWORK; FUZZY-LOGIC; TRUST; MODEL; ALGORITHM; TAG; EXPLANATIONS; FRAMEWORK;

D O I：

10.1016/j.knosys.2013.03.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet cif things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：109 / 132

页数：24

共 251 条

[81]

Felfernig A., 2008, 10 INT C EL COMM

[82]

Fengkun L., EXPERT SYSTEMS APPL, V37

[83]

Gao LQ, 2008, I C WIREL COMM NETW, P9215

[84] Userrank for item-based collaborative filtering recommendation [J].