Most of the previous approaches surrounding collaborative information retrieval (CIR) provide either a user-based mediation, in which the system only supports users' collaborative activities, or a system-based mediation, in which the system plays an active part in balancing user roles, reranking results, and distributing them to optimize overall retrieval performance. In this paper, we propose to combine both of these approaches by a role mining methodology that learns from users' actions about the retrieval strategy they adapt. This hybrid method aims at showing how users are different and how to use these differences for suggesting roles. The core of the method is expressed as an algorithm that (1) monitors users' actions in a CIR setting; (2) discovers differences among the collaborators along certain dimensions; and (3) suggests appropriate roles to make the most out of individual skills and optimize IR performance. Our approach is empirically evaluated and relies on two different laboratory studies involving 70 pairs of users. Our experiments show promising results that highlight how role mining could optimize the collaboration within a search session. The contributions of this work include a new algorithm for mining user roles in collaborative IR, an evaluation methodology, and a new approach to improve IR performance with the operationalization of user-driven system-mediated collaboration.