Recommender systems are designed to find items in which each user has most likely the highest interest. Items can be of any type such as commercial products, e-learning resources, movies, songs, and jokes. Successful web and mobile applications can collect easily thousands of users, thousands of items, and millions of item ratings in only few months. A solution to store and to process these continuously growing data is to build distributed recommender systems. The challenging task is to find the appropriate distribution strategy allowing an efficient retrieval of needed information. Considering the similarity between the task of predicting a rating, and the task of predicting a membership grade in graded multi-label classification (GMLC), we propose an adapted distribution strategy to efficiently build a decentralized recommender system based on GMLC.