Automatic user preference learning for personalized electronic program guide applications

Jeongyeon Lim, Sanggil Kang and Munchurl Kim

Journal of the American Society for Information Science and Technology, 2007, vol. 58, issue 9, 1346-1356

Abstract: 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.

Date: 2007
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https://doi.org/10.1002/asi.20577

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:58:y:2007:i:9:p:1346-1356

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