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A Content-based TV Program Recommender  

유상원 (서울대학교 전기.컴퓨터공학부)
이홍래 (서울대학교 전기.컴퓨터공학부)
이형동 (서울대학교 전기.컴퓨터공학부)
김형주 (서울대학교 전기.컴퓨터공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.9, no.6, 2003 , pp. 683-692 More about this Journal
Abstract
The rapid increase of the number of channels makes it hard to find wanted programs from TV. In recent years, the number of channels come up to hundreds with the digital TV arrival. So, it will drive us to the new way of watching TV. In this paper, we introduce a recommendation system for TV programs to overcome this difficulty. We model user profiles and design each module of the system, considering TV environment. Our system gathers basic information from people manually and then updates user profiles automatically by tracking viewing and usage history. As a result, our system recommends daily TV programs based on the changing interest of users. In this paper, we address the problems and solutions by describing our system and the experiment.
Keywords
TV; personalization; recommendation;
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  • Reference
1 Peter W. Foltz and Susan T. Dumais, Personalized Information Delivery: An Analyis of Information Filtering Methods, CACM 35(12) pp.51-60, 1992   DOI
2 Resnick, P., Iacovon, N., Sushak, M., Bergstrom, P., and Riedl, J., GroupLens: An open architecture for collaborative filltering of netnews. Proceedings of the 1994 Computer Supported Collaborative Work Conference. 1994   DOI
3 http://www.tv-anytime.org/
4 R. Korfhage, Information Storage and Retrieval, John Wiley, New York, 1997
5 Herlocker, J. Understadning and Improving Automated Collaborative Filtering Systems. Ph.D. Thesis, Computer Science Dept., University of Minnesota, 2000
6 Paul Resnick and Hal R. Varian, Recommender Systems, CACM 40(3) pp.56-58, 1997   DOI
7 Barry Smyth and Paul Cotter, Surfing the Digital Wave: Generating Personalised {TV} Listings Using Collaborative, Case-Based Recommendation, LNCS 1650, 1999
8 Marko Balabnovic and Yova Shoham, Fab: Content-Based, Collaborative Recommedndation, CACM 40(3) pp.66-72, 1997   DOI   ScienceOn
9 Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 1999
10 Breese, Heckermen and Kadie. Emperical analysis of predictive algorithms for collaborative filtering. Technical report, Microsoft Research, October 1998
11 J. J. Rocchio Relevance feedback in information retrieval. In The Smart System-Experiments in Automatic Document Processing, pp.313-323, Prentice Hall, 1971
12 http://www.tivo.com
13 Workshop on personalization in Future TV 2001/2002
14 P. Melville and R. Mooney and R. Nagarajan, Content-boosted collaborative filtering, SIGIR 2001
15 K. Kurapati and S. Gutta and D. Schaffer and J. Martino and J. Zimmerman, A multi-agent TV recommender, User Modeling, 2001
16 http://www.canalplus-technologies.com