On-line learning in neural networks
On-line learning in neural networks
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Pricing information bundles in a dynamic environment
Proceedings of the 3rd ACM conference on Electronic Commerce
Competitive market-based allocation of consumer attention space
Proceedings of the 3rd ACM conference on Electronic Commerce
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Congregating and market formation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Decision procedures for multiple auctions
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
A Platform for Electronic Commerce with Adaptive Agents
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
Challenges in Large-Scale Open Agent Mediated Economies
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Market-based recommendation: Agents that compete for consumer attention
ACM Transactions on Internet Technology (TOIT)
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A competitive distributed recommendation mechanism is introduced based on adaptive software agents for efficiently allocating the "customer attention space", or banners. In the example of an electronic shopping mall, the task of correctly profiling and analyzing the customers is delegated to the individual shops that operate in a distributed, remote fashion. The evaluation and classification of customers for the bidding on banners is not handled by a central agency as is customary, but is a distributed process where all shops bidding for a customer partake. This allows each agent for a shop to apply its own private strategy, learning-mechanism, and specific domain knowledge without revealing sensitive business operations to the central party. We present a scalable and extensible software agent architecture and prototype for distributed market-based allocation of customer attention space. The agents can operate in multiple markets concurrently. The protocol for communication between the agents is designed for optimal performance of the system.