Minimizing information overload: the ranking of electronic messages
Journal of Information Science
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Distributed rational decision making
Multiagent systems
MEMOIR — an open framework for enhanced navigation of distributed information
Information Processing and Management: an International Journal
Competitive market-based allocation of consumer attention space
Proceedings of the 3rd ACM conference on Electronic Commerce
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A market-based approach to recommender systems
ACM Transactions on Information Systems (TOIS)
Implicit: an agent-based recommendation system for web search
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Learning Users' Interests by Quality Classification in Market-Based Recommender Systems
IEEE Transactions on Knowledge and Data Engineering
Buyer agent to enhance consumer awareness: SAATHI
Electronic Commerce Research and Applications
A framework for agent-based distributed machine learning and data mining
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Epimethean information systems: harnessing the power of the collective in e-learning
International Journal of Information Technology and Management
Designing marketing experiences
Proceedings of the 7th ACM conference on Designing interactive systems
Dynamically transparent window
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Out of the box: exploring the richness of children's use of an interactive table
Proceedings of the 8th International Conference on Interaction Design and Children
Integrating information sources for recommender systems
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
A Fair Peer Selection Algorithm for an Ecommerce-Oriented Distributed Recommender System
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
MALEF: Framework for distributed machine learning and data mining
International Journal of Intelligent Information and Database Systems
Semantic web recommender system based personalization service for user XQuery pattern
WINE'05 Proceedings of the First international conference on Internet and Network Economics
A protocol for a distributed recommender system
Trusting Agents for Trusting Electronic Societies
Market-based recommender systems: learning users' interests by quality classification
AOIS'04 Proceedings of the 6th international conference on Agent-Oriented Information Systems II
Market-Inspired approach to collaborative learning
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Expert Systems with Applications: An International Journal
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Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed. However, it has been shown that no one technique is best for all users in all situations. Thus we believe that effective recommender systems should incorporate a wide variety of such techniques and that some form of overarching framework should be put in place to coordinate the various recommendations so that only the best of them (from whatever source) are presented to the user. To this end, we show that a marketplace, in which the various recommendation methods compete to offer their recommendations to the user, can be used in this role. Specifically, this paper presents the principled design of such a marketplace; detailing the auction protocol and reward mechanism and analyzing the rational bidding strategies of the individual recommendation agents.