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Recommender systems are systems that are capable of offering the mostsuitable services and products to users. Through specific methods andtechniques, the recommender systems try to identify the most appropriate items,such as types of information and goods and propose the closest to the user'stastes. Collaborative filtering offering active user suggestions based on therating of a set of users is one of the simplest and most comprehensible andsuccessful models for finding people in the same tastes in the recommendersystems. In this model, with increasing number of users and movie, the systemis subject to scalability. On the other hand, it is important to improve theperformance of the system when there is little information available on theratings. In this paper, a movie hybrid recommender system based on FNHSM_HRSstructure using resource allocation approach called FCNHSMRA_HRS is presented.The FNHSM_HRS structure was based on the heuristic similarity measure (NHSM),along with fuzzy clustering. Using the fuzzy clustering method in the proposedsystem improves the scalability problem and increases the accuracy of systemsuggestions. The proposed systems is based on collaborative filtering and, byusing the heuristic similarity measure and applying the resource allocationapproach, improves the performance, accuracy and precision of the system. Theexperimental results using MAE, Accuracy, Precision and Recall metrics based onMovieLens dataset show that the performance of the system is improved and theaccuracy of recommendations in comparison of FNHSM_HRS and collaborativefiltering methods that use other similarity measures for finding similarity, isincreased

FCNHSMRA_HRS: Improve the performance of the movie hybrid recommender system using resource allocation approach