Proceedings of the 6th international conference on Intelligent user interfaces
TV Scout: Lowering the Entry Barrier to Personalized TV Program Recommendation
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A recommender handoff framework for a mobile device
Mobile Multimedia Processing
Case study: recommending course reading materials in a small virtual learning community
International Journal of Web Based Communities
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The increasing amount of content available via digital television has made TV program recommenders valuable tools. In order to provide personalized recommendations, recommender systems need to collect information about user preferences. Since users are reluctant to invest much time in explicitly expressing their interests, preferences often need to be implicitly inferred through data gathered by monitoring user behavior. Which is, alas, less reliable. This article addresses the problem of learning TV preferences based on tracking the programs users have watched, whilst dealing with the varying degrees of reliability in such information. Three approaches to the problem are discussed: use all information equally; weight information by its reliability or simply discard the most unreliable information. Experimental results for these three approaches are presented and compared using a content-based filtering recommender built on a Naïve Bayes classifier.