  Nowadays, Recommender Systems have become a comprehensive system for helping
and guiding users in a huge amount of data on the Internet. Collaborative
Filtering offers to active users based on the rating of a set of users. One of
the simplest and most comprehensible and successful models is to find users
with a taste in recommender systems. In this model, with increasing number of
users and items, the system is faced to scalability problem. On the other hand,
improving system performance when there is little information available from
ratings, that is important. In this paper, a hybrid recommender system called
FNHSM_HRS which is based on the new heuristic similarity measure (NHSM) along
with a fuzzy clustering is presented. Using the fuzzy clustering method in the
proposed system improves the scalability problem and increases the accuracy of
system recommendations. The proposed system is based on the collaborative
filtering model and is partnered with the heuristic similarity measure to
improve the system's performance and accuracy. The evaluation of the proposed
system based results on the MovieLens dataset carried out the results using
MAE, Recall, Precision and Accuracy measures Indicating improvement in system
performance and increasing the accuracy of recommendation to collaborative
filtering methods which use other measures to find similarities.
