  Recommender systems are systems that are capable of offering the most
suitable services and products to users. Through specific methods and
techniques, the recommender systems try to identify the most appropriate items,
such as types of information and goods and propose the closest to the user's
tastes. Collaborative filtering offering active user suggestions based on the
rating of a set of users is one of the simplest and most comprehensible and
successful models for finding people in the same tastes in the recommender
systems. In this model, with increasing number of users and movie, the system
is subject to scalability. On the other hand, it is important to improve the
performance of the system when there is little information available on the
ratings. In this paper, a movie hybrid recommender system based on FNHSM_HRS
structure 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 proposed
system improves the scalability problem and increases the accuracy of system
suggestions. The proposed systems is based on collaborative filtering and, by
using the heuristic similarity measure and applying the resource allocation
approach, improves the performance, accuracy and precision of the system. The
experimental results using MAE, Accuracy, Precision and Recall metrics based on
MovieLens dataset show that the performance of the system is improved and the
accuracy of recommendations in comparison of FNHSM_HRS and collaborative
filtering methods that use other similarity measures for finding similarity, is
increased
