Distributed Recommender Profiling and Selection with Gittins Indices

Most 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 to share their resources and recommendations. In this paper, we present a distributed recommender system model that consists of multiple recommender systems from different organizations. With the hope to provide better recommendation service to users, the recommender systems can improve their performances by sharing their recommendations cooperatively. A recommender selection technique based on the Gittins indices is presented in this paper, and it makes selections based on the stability, average performance and selection frequency of the recommenders