Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Proceedings of the 6th international conference on Intelligent user interfaces
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Using Collaborative Filtering Data in Case-Based Recommendation
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Capturing task knowledge for geo-spatial imagery
Proceedings of the 2nd international conference on Knowledge capture
User evaluation of Físchlár-News: An automatic broadcast news delivery system
ACM Transactions on Information Systems (TOIS)
Task-based annotation and retrieval for image information management
Multimedia Tools and Applications
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In this paper, we evaluate the use of implicit interest indicators as the basis for user profiling in the Digital TV domain. Research in more traditional domains, such as Web browsing or Usenet News, indicates that some implicit interest indicators (e.g., read-time and mouse movements) are capable of serving as alternative to explicit profile information such as user ratings. Consequently, the key question we wish to answer relates to the type of implicit indicators that can be identified within the DTV domain and the extent to which they can accurately reflect a user's true preferences.