A collaborative filtering similarity measure based on singularities

被引：123

作者：

Bobadilla, Jesus ^{[1
]}

Ortega, Fernando ^{[1
]}

Hernando, Antonio ^{[1
]}

机构：

[1] Univ Politecn Madrid, FilmAffin Com Res Team, Madrid 28031, Spain

来源：

INFORMATION PROCESSING & MANAGEMENT | 2012年 / 48卷 / 02期

关键词：

Collaborative filtering; Recommender systems; Singularity; Similarity measures; Neighborhoods; OF-THE-ART; RECOMMENDER SYSTEMS;

D O I：

10.1016/j.ipm.2011.03.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recommender systems play an important role in reducing the negative impact of information overload on those websites where users have the possibility of voting for their preferences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to calculate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movie lens, Netflix and Film Affinity databases, corroborate the excellent behaviour of the singularity measure proposed. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：204 / 217

页数：14

共 32 条

[1] Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].

Adomavicius, G ;

Tuzhilin, A .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (06) :734-749

[2] Fuzzy-genetic approach to recommender systems based on a novel hybrid user model [J].

Al-Shamri, Mohammad Yahya H. ;

Bharadwaj, Kamal K. .

EXPERT SYSTEMS WITH APPLICATIONS, 2008, 35 (03) :1386-1399

[3]

[Anonymous], 2007, P 2007 INT C SERV SY, DOI DOI 10.1109/ICSSSM.2007.4280214

[4]

[Anonymous], P 31 ANN M OH STAT U

[5]

Baltrunas L, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P295

[6] A new collaborative filtering metric that improves the behavior of recommender systems [J].

Bobadilla, J. ;

Serradilla, F. ;

Bernal, J. .

KNOWLEDGE-BASED SYSTEMS, 2010, 23 (06) :520-528

[7] Collaborative filtering adapted to recommender systems of e-learning [J].

Bobadilla, J. ;

Serradilla, F. ;

Hernando, A. .

KNOWLEDGE-BASED SYSTEMS, 2009, 22 (04) :261-265

[8]

Candillier L, 2007, LECT NOTES ARTIF INT, V4571, P548

[9] Collaborative filtering using orthogonal nonnegative matrix tri-factorization [J].

Chen, Gang ;

Wang, Fei ;

Zhang, Changshui .

INFORMATION PROCESSING & MANAGEMENT, 2009, 45 (03) :368-379

[10]

Denis H., 2007, P 29 INT C INF TECHN, P345

← 1 2 3 4 →