Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
The personal electronic program guide—towards the pre-selection of individual TV programs
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Communications of the ACM
ACM president's letter: electronic junk
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Ganging up on Information Overload
Computer
Merging Ranks from Heterogeneous Internet Sources
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Using a Painting Metaphor to Rate Large Numbers of Objects
Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces-Volume I - Volume I
Intelligent Media Agents in Interactive Television Systems
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Proceedings of the 8th international interactive conference on Interactive TV&Video
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In this paper, we present TV Scout, a recommendation system providing users with personalized TV schedules. The TV Scout architecture addresses the “cold-start” problem of information filtering systems, i.e. that filtering systems have to gather information about the user's interests before they can compute personalized recommendations. Traditionally, gathering this information involves upfront user effort, resulting in a substantial entry barrier. TV Scout is designed to avoid this problem by presenting itself to new users not as a filtering system, but as a retrieval system where all user effort leads to an immediate result. While users are dealing with this retrieval functionality, the system continuously and unobtrusively gathers information about the user's interests from implicit feedback and gradually evolves into a filtering system. An analysis of log file data gathered with over 10,000 registered online users shows that over 85% of all first-time users logged in again, suggesting that the described architecture is successful in lowering the entry barrier.