  With the growing data on the Internet, recommender systems have been able to
predict users' preferences and offer related movies. Collaborative filtering is
one of the most popular algorithms in these systems. The main purpose of
collaborative filtering is to find the users or the same items using the rating
matrix. By increasing the number of users and items, this algorithm suffers
from the scalability problem. On the other hand, due to the unavailability of a
large number of user preferences for different items, there is a cold start
problem for a new user or item that has a significant impact on system
performance. The purpose of this paper is to design a movie recommender system
named TRSM-RS using users' demographic information (just users' gender) along
with the new weighted similarity measure. By segmenting users based on their
gender, the scalability problem is improved, and by considering the reliability
of the users' similarity as the weight in the new similarity measure (Tanimoto
Reliability Similarity Measure, TRSM), the effect of the cold-start problem is
undermined and the performance of the system is improved. Experiments were
performed on the MovieLens dataset and the system was evaluated using mean
absolute error (MAE), Accuracy, Precision, and Recall metrics. The results of
the experiments indicate improved performance (accuracy and precision) and
system error rate compared to other research methods of the researchers. The
maximum improved MAE rate of the system for men and women is 5.5% and 13.8%,
respectively.
