Presentation 2010-06-25
User Feedback Utilization and Query Destination Learning Methods for Peer-to-Peer Information Retrieval-an Evaluation
Tsunenori MINE, Hirotake KOBAYASHI,
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Abstract(in English) This paper proposes a novel Peer-to-Peer Information Retrieval method using user feedback and query-destination-learning. The method utilize negative feedback information actively so that other agents can filter out irrelevant documents with it. Using query-destination-learning, the method can accumulate relevant information from all the member agents in a community. It can also reduce communication loads by caching queries and their sender-responder agent addresses in the community. The experimental results illustrated the validity of the proposed methods.
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Keyword(in English) Multi-Agent / Community / Information Retrieval / Peer-to-Peer / User Feedback
Paper # AI2010-8
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Committee AI
Conference Date 2010/6/18(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) User Feedback Utilization and Query Destination Learning Methods for Peer-to-Peer Information Retrieval-an Evaluation
Sub Title (in English)
Keyword(1) Multi-Agent
Keyword(2) Community
Keyword(3) Information Retrieval
Keyword(4) Peer-to-Peer
Keyword(5) User Feedback
1st Author's Name Tsunenori MINE
1st Author's Affiliation Dept. of Advanced Information Technology, Faculty of ISEE, Kyushu University()
2nd Author's Name Hirotake KOBAYASHI
2nd Author's Affiliation Dept. of Advanced Information Technology, Graduate School of ISEE, Kyushu University
Date 2010-06-25
Paper # AI2010-8
Volume (vol) vol.110
Number (no) 105
Page pp.pp.-
#Pages 6
Date of Issue