Most of the existing recommender systems nowadays operate in a single organizational base, and very often they do not have sufficient resources to be used in order to generate quality recommendations. Therefore, it would be beneficial if recommender systems of different organizations can cooperate together sharing their resources and recommendations. In this paper, we propose a preliminary design of a distributed recommender system that consists of multiple recommender systems from different organizations. Moreover, a peer selection algorithm is also presented that allows a recommender system peer to select a set of other peers to cooperate with. The proposed selection mechanism not only ensures a high degree of user satisfaction to the generated recommendation, it also makes sure that every peer has been fairly treated and studied. The paper also further points out how the proposed distributed recommender system and the peer selection algorithm can provide a solution to the problem of resource lacking (e.g. cold start problem) and also enables recommender systems to provide recommendations with better novelty and quality to users.