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EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Collaborative Case-Based Reasoning: Applications in Personalised Route Planning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
CoWing: A Collaborative Bookmark Management System
CIA '01 Proceedings of the 5th International Workshop on Cooperative Information Agents V
A reputation system for peer-to-peer networks
NOSSDAV '03 Proceedings of the 13th international workshop on Network and operating systems support for digital audio and video
Learning to form dynamic committees
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Computing social networks for information sharing: a case-based approach
OCSC'07 Proceedings of the 2nd international conference on Online communities and social computing
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In this paper, we describe a cooperative P2P bibliographical data management and recommendation system (COBRAS). In COBRAS, each user is assisted by a personal software agent that helps her/him to manage bibliographical data and to recommend new bibliographical references that are known by peer agents. Key problems are: – how to obtain relevant references? – how to choose a set of peer agents that can provide the most relevant recommendations? Two inter-related case-based reasoning (CBR) components are proposed to handle both of the above mentioned problems. The first CBR is used to search, for a given user's interest, a set of appropriate peers to collaborate with. The second one is used to search for relevant references from the selected agents. Thus, each recommender agent proposes not only relevant references but also some agents which it judges to be similar to the initiator agent. Our experiments show that using a CBR approach for committee and reference recommendation allows to enhance the system overall performances by reducing network load (i.e. number of contacted peers, avoiding redundancy) and enhancing the relevance of computed recommendations by reducing the number of noisy recommendations.