ABSTRACT
We propose and evaluate the performance of a number of methods for automatic recording of TV programs for digital video servers, which estimate the user's preference over TV programs based on her/his past viewing behavior and automatically record a selected number of TV programs believed to be of interest to the user. Our methods combine the so-called content-based filtering and social (or collaborative) filtering methods and are based on a certain class of on-line learning algorithms known as the `specialist' algorithms, recently developed in the field of computational learning theory. We empirically evaluated the performance of content-based part of the proposed methods using preference data on TV programs consisting of scores given by people on actual TV programs. The results are largely encouraging and indicate in particular that our methods are practical in terms of both the precision in predicting the user's preference and computational complexity.
- 1.Tivo: Introducing the new face of television. http://www.tivo.com/.Google Scholar
- 2.P. Bandisch. Tv-online: An adaptive tv-program guide on the world wide web. In Proceedings of the ABIS'96 Workshop, pages D5.1-D5.4, 1996.Google Scholar
- 3.W. Cohen and Y. Singer. Context-sensitive learning methods for text categorization. In Proceedings of SIGIR'96, pages 307-315, 1996. Google ScholarDigital Library
- 4.Y. Freund, R. Schapire, Y. Singer, and M. Warmuth. Using and combining predictors that specialize. In Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, pages 334-343, 1997. Google ScholarDigital Library
- 5.M. R. Garey and D. S. Johnson. Computers and Intractability - A guide to the theory of NP-completeness. W. H. Freeman and Company, New York, N.Y., 1979. Google ScholarDigital Library
- 6.L. Kaelbling. Learning in Embedded Systems. PhD thesis, Stanford University, 1990. Google ScholarDigital Library
- 7.T. Kamba, H. Sakagami, and Y. Koseki. Anatagonomy: a personalized newspaper on the world wide web ? International Journal on Human-Computer Studies, June 1997. Google ScholarDigital Library
- 8.K. Lang. Newsweeder: Learning to filter netnews. In Proceedings of the 1Pth International Conference on Machine Learning, pages 331-339, 1995.Google ScholarCross Ref
- 9.A. Nakamura and N. Abe. Collaborative filtering using weighted majority prediction algorithms. In Proceedings of The 15th International Conference on Machine Learning, pages 395-403, 1998. Google ScholarDigital Library
- 10.K. Ochiai, H. Matoba, and K. Maeno. @randomtv: A following generation tv program-viewing system using a random access device. In 56th Annual Conference Proceedings of Information Processing Society of Japan. in Japanese.Google Scholar
- 11.P. Resnick, N. Iacovou, M. Suchak, P. Bergstom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of CSCW, pages 175-186, 1994. Google ScholarDigital Library
- 12.U. Shardanand and P. Maes. Social information filtering: Algorithms and automating "word of mouth". In Proc. of CHI95, pages 210-217, 1995. Google ScholarDigital Library
Index Terms
- Automatic recording agent for digital video server
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