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In this paper, we present COBRAS: a CBR-based peer-to-peer bibliographical reference recommender system. The system allows a group of like-minded people to share their bibliographical data in an implicit and intelligent way. The system associates a software agent with each user. Agents are attributed three main skills: a) detecting the associated user hot topics, b) selecting a subset of peer agents that are likely to provide relevant recommendations and c) recommending both documents and other agents in response to a recommendation request sent by a peer agent. The last two skills are handled by two inter-related data-driven case-based reasoning sub-systems. This paper focuses on the design and the implementation of these two sub-systems. An experimental study involving one hundred software agents using real bibliographical data is described. Obtained results assess the validity of the proposed approach.