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Nowadays, Recommender Systems have become a comprehensive system for helpingand guiding users in a huge amount of data on the Internet. CollaborativeFiltering offers to active users based on the rating of a set of users. One ofthe simplest and most comprehensible and successful models is to find userswith a taste in recommender systems. In this model, with increasing number ofusers and items, the system is faced to scalability problem. On the other hand,improving system performance when there is little information available fromratings, that is important. In this paper, a hybrid recommender system calledFNHSM_HRS which is based on the new heuristic similarity measure (NHSM) alongwith a fuzzy clustering is presented. Using the fuzzy clustering method in theproposed system improves the scalability problem and increases the accuracy ofsystem recommendations. The proposed system is based on the collaborativefiltering model and is partnered with the heuristic similarity measure toimprove the system's performance and accuracy. The evaluation of the proposedsystem based results on the MovieLens dataset carried out the results usingMAE, Recall, Precision and Accuracy measures Indicating improvement in systemperformance and increasing the accuracy of recommendation to collaborativefiltering methods which use other measures to find similarities.

FNHSM_HRS: Hybrid recommender system using fuzzy clustering and heuristic similarity measure