Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating

Hyoseop Shin, Minsoo Lee, Eun Yi Kim

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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.

Original languageEnglish
Pages (from-to)1417-1423
Number of pages7
JournalIEEE Transactions on Consumer Electronics
Volume55
Issue number3
DOIs
StatePublished - 2009

Keywords

  • Classification
  • Content rating
  • Content recommendation
  • Digital TV
  • Keyword-based rating
  • User profiling

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