Exploiting learning techniques for the acquisition of user stereotypes and communities
UM '99 Proceedings of the seventh international conference on User modeling
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Acquiring User Preferences for Information Filtering in Interactive Multi-Media Services
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
A multi-agent system for E-insurance brokering
NODe'02 Proceedings of the NODe 2002 agent-related conference on Agent technologies, infrastructures, tools, and applications for E-services
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Almost all insurers now have a web site providing information on the company, its products and contact details, but the scope of insurance activities potentially affected by e-commerce is much broader than that, giving rise to a host of interesting issues. Also, customers buying over the Internet look for added value. This paper proposes a distributed multi-agent system for e-insurance brokering where customers are grouped together, exploiting user modelling and machine learning techniques, as an approach to better match customers and insurance product offers. Particular attention is paid to the interpretation of the generated communities. For this purpose, we use a metric to identify the representative insurance product configuration of each community. To improve broker's evaluation of received insurers' bids we propose the automatic construction of the customer's profile, reflecting its preferences on all the attributes of an insurance product.