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Journal of Electronic Imaging

Modeling the user preference of broadcasting content using Bayesian networks
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Paper Abstract

We introduce a novel model capturing user preference using the Bayesian approach for recommending users' preferred multimedia content. Unlike other preference models, our method traces the trend of a user preference in time. It allows us to do online learning so we do not need 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 modification is done by partitioning usage history data into smaller sets in a time axis and then weighting the frequencies of attributes to be computed from the partitioned sets of the usage history data in order to differently reflect their significance on predicting the future preference. In the experimental section, the learning and reasoning on user preference in genres are performed by the proposed method with a set of real TV viewers' watching history data collected from many real households. The reasoning performance by the proposed method is also compared with that by a typical method without training in order to show the superiority of our proposed method.

Paper Details

Date Published: 1 April 2005
PDF: 10 pages
J. Electron. Imag. 14(2) 023022 doi: 10.1117/1.1922127
Published in: Journal of Electronic Imaging Volume 14, Issue 2
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)

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