Lim, J.; Kang, S.; Kim, M.: Automatic user preference learning for personalized electronic program guide applications (2007)
0.01
0.0063645868 = product of:
0.01909376 = sum of:
0.01909376 = product of:
0.057281278 = sum of:
0.057281278 = weight(_text_:network in 476) [ClassicSimilarity], result of:
0.057281278 = score(doc=476,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.29521978 = fieldWeight in 476, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=476)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- 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.
