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MarCol: A Market-Based Recommender System
May/June 2007 (vol. 22 no. 3)
pp. 74-78
Dan Melamed, Ben-Gurion University of the Negev
Bracha Shapira, Ben-Gurion University of the Negev
Yuval Elovici, Ben-Gurion University of the Negev
Collaborative information-filtering systems recommend relevant items to users on the basis of their common interests. The users express their interests by leaving relevance feedback on items. The system's ability to learn user preferences and predict accurate recommendations depends on the number of judgments the user provides. However, users tend to "free-ride," consuming other users' judgments without providing their own. To solve this problem, systems should offer users incentives for providing judgments. A new market-based model for pricing judgments aims to motivate users by requiring them to provide judgments before they can receive recommendations. Researchers used MarCol, a market-based collaborative IF system, to conduct experiments examining the model's effect on user feedback provision, user satisfaction, and recommendation quality. Results show that the model increases feedback and improves recommendation quality. This article is part of a special issue on Recommender Systems.
Index Terms:
information filtering, performance judgment, relevance feedback
Citation:
Dan Melamed, Bracha Shapira, Yuval Elovici, "MarCol: A Market-Based Recommender System," IEEE Intelligent Systems, vol. 22, no. 3, pp. 74-78, May-June 2007, doi:10.1109/MIS.2007.57
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