ユーザフィードバックとクエリ学習手法の複数コミュニティ上での評価

概要

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This paper proposes a novel Peer-to-Peer Information Retrieval (P2PIR) method using user feedback and query-destination-learning. The method uses positive feedback information effectively for getting documents relevant to a query by giving higher score to them. The method also utilizes negative feedback information actively so that other agents can filter it out with itself. Using query-destination-learning, the method can not only accumulate relevant information from all the member agents in a community, but also reduce communication loads by caching queries and their sender-responder agent addresses in the community. Experiments were carried out on both single and multiple communities constructed with multi-agent framework Kodama. The experimental results illustrated that the proposed method effectively increased retrieval accuracy.