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Multiagent systems: a modern approach to distributed artificial intelligence
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Recommender systems in e-commerce
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Dynamic pricing by software agents
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Competitive market-based allocation of consumer attention space
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Genetic Algorithms in Search, Optimization and Machine Learning
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ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Competing sellers in online markets: reserve prices, shill bidding, and auction fees
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Computers in Human Behavior
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Sellers competing for buyers in online markets: reserve prices, shill bids, and auction fees
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Competing sellers in online markets: reserve prices, shill bidding, and auction fees
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Information market based recommender systems fusion
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ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Web Intelligence and Agent Systems
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The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the "consumer attention space," or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of "consumer attention space" for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding-strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains.