Modeling the user preference on broadcasting contents using Bayesian networks

Sanggil Kang; Jeongyeon Lim; Munchurl Kim

doi:10.1117/12.526264

18 January 2004 Modeling the user preference on broadcasting contents using Bayesian networks

Sanggil Kang, Jeongyeon Lim, Munchurl Kim

Proceedings Volume 5308, Visual Communications and Image Processing 2004; (2004) https://doi.org/10.1117/12.526264
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

In this paper, we introduce a new supervised learning method of a Bayesian network for user preference models. Unlike other preference models, our method traces the trend of a user preference as time passes. It allows us to do online learning so we do not need the exhaustive data collection. The tracing of the trend can be done by modifying the frequency of attributes in order to force the old preference to be correlated with the current preference under the assumption that the current preference is correlated with the near future preference. The objective of our learning method is to force the mutual information to be reinforced by modifying the frequency of the attributes in the old preference by providing weights to the attributes. With developing mathematical derivation of our learning method, experimental results on the learning and reasoning performance on TV genre preference using a real set of TV program watching history data.

Citation Download Citation

Sanggil Kang, Jeongyeon Lim, and Munchurl Kim "Modeling the user preference on broadcasting contents using Bayesian networks", Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); https://doi.org/10.1117/12.526264

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