Fab: content-based, collaborative recommendation
Communications of the ACM
A first step to formally evaluate collaborative work
GROUP '97 Proceedings of the international ACM SIGGROUP conference on Supporting group work: the integration challenge
Agents teaching agents to share meaning
Proceedings of the fifth international conference on Autonomous agents
Machine Learning
An agent-based approach to knowledge management
Proceedings of the eleventh international conference on Information and knowledge management
Design and evaluation of a multi-agent collaborative Web mining system
Decision Support Systems - Web retrieval and mining
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Enriching Information Agents' Knowledge by Ontology Comparison: A Case Study
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
QueryTracker: An Agent for Tracking Persistent Information Needs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
User Profiling for Web Page Filtering
IEEE Internet Computing
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Personal information agents emerged in the last decade as an alternative to assist users to cope with the increasing volume of information available on the Web. In order to provide personalized assistance, these agents rely on user profiles modeling user information preferences, interests and habits. Inserted in communities of people with similar interests, personal agents can improve their assistance by gathering knowledge extracted from the observed common behaviors of single users. In this paper we propose an agent-based recommender system for supporting collaborative Web search in groups of users with partial similarity of interests. Empirical evaluation demonstrates that the interaction among personal agents increases the performance of the overall recommender system.