Automatic user preference learning for personalized electronic program
guide applications
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 58 (9):
1346-1356 (July 2007)DOI: {10.1002/asi.20577}
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.
Links and resources
Tags
community
@datentaste's tags highlighted