Agents that reduce work and information overload
Communications of the ACM
Agent theories, architectures, and languages: a survey
ECAI-94 Proceedings of the workshop on agent theories, architectures, and languages on Intelligent agents
Fab: content-based, collaborative recommendation
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
A market-based approach to recommender systems
ACM Transactions on Information Systems (TOIS)
Agents' roles in B2C e-commerce
AI Communications
MASHA: A multi-agent system handling user and device adaptivity of Web sites
User Modeling and User-Adapted Interaction
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evaluation of attribute-aware recommender system algorithms on data with varying characteristics
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Among the Web opportunities, e-Commerce processes have increased in relevance requiring the development of complex tools to support all the parties involved therein. This paper proposes a neural network hybrid recommender system able to provide customers, associated with XML-based personal agents within a multi-agent system called MARF, with suggestions about flights purchases. MARF agents continuously monitor customers' interests and preferences in their commercial Web activities, by constructing and automatically maintaining their profiles. In order to highlight the benefits provided by the proposed flight recommender, some experimental results carried out by exploiting a MARF prototype are presented.