Automatic user preference learning for personalized electronic program guide applications
✍ Scribed by Jeongyeon Lim; Sanggil Kang; Munchurl Kim
- Publisher
- John Wiley and Sons
- Year
- 2007
- Tongue
- English
- Weight
- 905 KB
- Volume
- 58
- Category
- Article
- ISSN
- 1532-2882
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✦ Synopsis
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.