Abstract
The rapid growth of communication technologies and the invention of set-top-box (STB) and personal digital recorder (PDR) have enabled today’s television to receive and store tremendous programs. The abundance of TV programs precipitates a need for personalization tools to help people obtain programs that they really want to watch. User preference learning plays an important role in TV program personalization. In this paper, we introduce a hybrid user preference learning approach for TV program personalization. The learning architecture is designed to integrate multiple learning sources for preference learning, which are explicit input/modification, user viewing history, and user real-time feedback. Among those, learning from user viewing history and learning from user real-time feedback are described in detail. The experimental results proved that the hybrid learning approach outperforms the learning method merely adopting user real-time feedback.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Das, D., ter Horst, H.: Recommender Systems for TV. In: Proc. of AAAI (1998)
Cotter, P., Smyth, B.: PTV: Personalised TV Guides. In: Proc. of the 12th Conf. on Innovative Applications of Artificial Intelligence, IAAI 2000, Austin, Texas (2000)
Kurapati, K., Gutta, S., Schaffer, D., Martino, J., Zimmerman, J.: A Multi-Agent TV Recommender. In: Proc. of the User Modeling 2001: Personalization in Future TV Workshop, Sonthofen, Germany (2001)
Ehrmantraut, M., Harder, T., Wittig, H., Steinmetz, R.: The Personal Electronic Program Guide - Towards the Pre-Selection of Individual TV Programs. In: Proc. of CIKM 1996, Rockville, Maryland, USA, pp. 243–250 (1996)
Salton, G.: Automatic Text Processing: The transformation, analysis, and retrieval of information by computer. Addison-Wesley, Massachusetts, USA (1989)
Rocchio, J.J.: Relevance feedback in information retrieval. In: The Smart System–Experiments in Automatic Document Processing, Prentice-Hall, Englewood Cliffs (1971)
Joachims, T.: A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization. In: Proc. of the 14th Intl Conf. on Machine Learning, pp. 143–151 (1997)
Lidstone, G.J.: Note on the general case of the Bayes-Laplace formula for inductive or a posteriori probabilities. Transactions of the Faculty of Actuaries 8, 182–192
van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths (1979)
Zhou, X.S., Yu, Z.W., Gu, J.H., Wu, X.J., Zhang, Y.: A Multi-Agent System for Personalized and Private Service in PDR. In: Proc. of ITCC2003, Las Vegas, Nevada, USA, pp. 635–639 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, Z., Zhou, X., Yang, Z. (2004). A Hybrid Learning Approach for TV Program Personalization. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_87
Download citation
DOI: https://doi.org/10.1007/978-3-540-30132-5_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
eBook Packages: Springer Book Archive