Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
STARTS: Stanford proposal for Internet meta-searching
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Effective retrieval with distributed collections
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Methods for information server selection
ACM Transactions on Information Systems (TOIS)
A decision-theoretic approach to database selection in networked IR
ACM Transactions on Information Systems (TOIS)
User interactions with everyday applications as context for just-in-time information access
Proceedings of the 5th international conference on Intelligent user interfaces
A reinforcement learning agent for personalized information filtering
Proceedings of the 5th international conference on Intelligent user interfaces
Collection selection and results merging with topically organized U.S. patents and TREC data
Proceedings of the ninth international conference on Information and knowledge management
A vector space model for automatic indexing
Communications of the ACM
Evaluating different methods of estimating retrieval quality for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
A Multi-Agent Approach for Peer-to-Peer Based Information Retrieval System
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Unified utility maximization framework for resource selection
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Modeling search engine effectiveness for federated search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
User modeling for full-text federated search in peer-to-peer networks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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This paper proposes a new approach using a multi agents system for personalising the distributed information retrieval (DIR). Most prior research in DIR focused on selecting and merging information that has the most relevant content according to the query, but ignored the user's specific needs. Our approach extends the state-of-the-art in a DIR. Firstly, it develops an agent called user-agent for managing the user profile. Secondly, it develops an agent called source-agent for each information source in order to manage its information source. Finally, it develops an agent called agent-broker for cooperating between user-agent and each source-agent in order to select the best source and merge their best documents in response to the user's query. The approach has been experimented with several known information sources. The experimental results show that the approach: (1) improve the relevance of the result, (2) reduce the response times and (3) improve the system extensibility.