What's on TV Tonight? An Efficient and Effective Personalized Recommender System of TV Programs

被引：46

作者：

Barragans Martinez, Ana Belen ^{[1
]}

Pazos Arias, Jose J. ^{[2
]}

Fernandez Vilas, Ana ^{[1
]}

Garcia Duque, Jorge ^{[1
]}

Lopez Nores, Martin ^{[1
]}

机构：

[1] Univ Vigo, Dept Telemat Engn, Vigo, Spain

[2] Univ Vigo, Networking & Software Engn Grp, Vigo, Spain

来源：

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS | 2009年 / 55卷 / 01期

关键词：

Recommender systems; Contents personalization; Collaborative filtering; SVD; COLLABORATIVE RECOMMENDATION; ALGORITHMS;

D O I：

10.1109/TCE.2009.4814447

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

With the expansion of digital networks and TV devices and the rapid increase of the number of channels, people are exposed to an information overload, due to the presence of several hundreds of alternative programs to watch. In this context, personalization is achieved with the employment of algorithms and data collection schemes that predict and recommend to television viewers content that match their interests and/or needs. This paper introduces queveo.tv: a personalized TV program recommendation system. The proposed hybrid approach (combining content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network as comments, tagging, ratings, etc. This web 2.0 application has been devised to enormously simplify the task of selecting what program to watch on TV.

引用

页码：286 / 294

页数：9

共 27 条

[1] Incorporating contextual information in recommender systems using a multidimensional approach [J].

Adomavicius, G ;

Sankaranarayanan, R ;

Sen, S ;

Tuzhilin, A .

ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2005, 23 (01) :103-145

[2] 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

[3]

[Anonymous], 2006, P SIGCHI C HUM FACT, DOI DOI 10.1145/1124772.1124915

[4] Fab: Content-based, collaborative recommendation [J].

Balabanovic, M ;

Shoham, Y .

COMMUNICATIONS OF THE ACM, 1997, 40 (03) :66-72

[5]

Basu C, 1998, FIFTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-98) AND TENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICAL INTELLIGENCE (IAAI-98) - PROCEEDINGS, P714

[6]

Baudisch P., 2001, GMD RES SERIES

[7] Hybrid recommender systems: Survey and experiments [J].

Burke, R .

USER MODELING AND USER-ADAPTED INTERACTION, 2002, 12 (04) :331-370

[8] Eigentaste: A constant time collaborative filtering algorithm [J].

Goldberg, K ;

Roeder, T ;

Gupta, D ;

Perkins, C .

INFORMATION RETRIEVAL, 2001, 4 (02) :133-151

[9]

Golub G. H., 2012, Matrix computations, V4th

[10]

HARPER M, 2007, 2 INT C PERS TECHN P, P148

← 1 2 3 →