Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and pro-viding a ranked list of documents adapted for each collaborator. In this paper, we introduce the first document-ranking model supporting collab-oration between two users characterized by roles relying on different do-main expertise levels. Specifically, we propose a two-step ranking model: we first compute a document-relevance score, taking into consideration do-main expertise-based roles. We introduce specificity and novelty factors into language-model smoothing, and then we assign, via an Expectation-Maximization algorithm, documents to the best-suited collaborator. Our experiments employ a simulation-based framework of collaborative informa-tion retrieval and show the significant effectiveness of our model at different search levels.