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Proceedings Paper

Modeling the user preference on broadcasting contents using Bayesian networks
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Paper 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.

Paper Details

Date Published: 18 January 2004
PDF: 10 pages
Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); doi: 10.1117/12.526264
Show Author Affiliations
Sanggil Kang, Information and Communications Univ. (South Korea)
Jeongyeon Lim, Information and Communications Univ. (South Korea)
Munchurl Kim, Information and Communications Univ. (South Korea)


Published in SPIE Proceedings Vol. 5308:
Visual Communications and Image Processing 2004
Sethuraman Panchanathan; Bhaskaran Vasudev, Editor(s)

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