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Personalized Digital TV Content Recommendation with Integration of User Behavior Profiling and Multimodal Content Rating.
- Source :
- IEEE Transactions on Consumer Electronics; Aug2009, Vol. 55 Issue 3, p1417-1423, 7p
- Publication Year :
- 2009
-
Abstract
- This paper presents the novel development of an embedded system that aims at digital TV content recommendation based on descriptive metadata collected from versatile sources. The described system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. TV content items are ranked using a combined multimodal approach integrating classification-based and keyword-based similarity predictions so that a user is presented with a limited subset of relevant content. Observable user behaviors are discussed as instrumental in user profiling and a formula is provided for implicitly estimating the degree of user appreciation of content. A new relation-based similarity measure is suggested to improve categorized content rating precision. Experimental results show that our system can recommend desired content to users with significant amount of accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00983063
- Volume :
- 55
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Consumer Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 44733955
- Full Text :
- https://doi.org/10.1109/TCE.2009.5278008