Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Capturing human intelligence in the net
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Why batch and user evaluations do not give the same results
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of extrinsic motivation on user behavior in a collaborative information finding system
Journal of the American Society for Information Science and Technology
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Novel Web Page Analysis Method for Efficient Reasoning of User Preference
APCHI '08 Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction
SXRS: An XLink-based Recommender System using Semantic Web technologies
Expert Systems with Applications: An International Journal
Recommendation method that considers the context of product purchases
WSEAS Transactions on Information Science and Applications
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
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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.