Automatic user preference learning for personalized electronic program guide applications

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In this article, we introduce a user preference model contained in the User Interaction Tools Clause of the MPEG-7 Multimedia Description Schemes, which is described by a UserPreferences description scheme (DS) and a UsageHistory description scheme (DS). Then we propose a user preference learning algorithm by using a Bayesian network to which weighted usage history data on multimedia consumption is taken as input. Our user preference learning algorithm adopts a dynamic learning method for learning real-time changes in a user's preferences from content consumption history data by weighting these choices in time. Finally, we address a user preference-based television program recommendation system on the basis of the user preference learning algorithm and show experimental results for a large set of realistic usage-history data of watched television programs. The experimental results suggest that our automatic user reference learning method is well suited for a personalized electronic program guide (EPG) application.
Publisher
JOHN WILEY & SONS INC
Issue Date
2007-07
Language
English
Article Type
Article
Citation

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, v.58, no.9, pp.1346 - 1356

ISSN
1532-2882
DOI
10.1002/asi.20577
URI
http://hdl.handle.net/10203/88942
Appears in Collection
EE-Journal Papers(저널논문)
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