Collaborative Filtering (CF) is one of the principal techniques applied in Recommender Systems, which uses ratings from similar users to predict interest items to a particular user. The scalability issue is a widespread problem of CF. The clustering technique is a successful approach to address the scalability issue in CF. However, some classic clustering methods cannot find appropriate clusters, which leads to low prediction accuracy. This paper suggests a new clustering algorithm based on the Learning Automata (LA) framework to group users for the CF technique. In this algorithm, a learning automaton is assigned to each user to detect the cluster membership of that user. Learning automatons improve their selection based on the reinforcement signal is received from intra-cluster distances and inter-cluster distances in previous iterations.Experimental results on standard and real datasets show that the proposed algorithm outperforms other compared methods in various evaluation metrics. This approach enhances the prediction accuracy and effectively deals with the scalability problem. Manuscript profile