Learning in embedded systems
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Machine Learning
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Recommender systems: a market-based design
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Guest Editors' Introduction: Information Enhancement for Data Mining
IEEE Intelligent Systems
Market-based recommendation: Agents that compete for consumer attention
ACM Transactions on Internet Technology (TOIT)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Market-based recommender systems: learning users' interests by quality classification
AOIS'04 Proceedings of the 6th international conference on Agent-Oriented Information Systems II
Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Designing business-intelligence tools with value-driven recommendations
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
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Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users' ratings of their suggestions. Moreover, we have theoretically shown how our system incites the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users' interests for these different levels, and then adapt its bidding behavior for the various levels accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users' interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations.