  How to make the best decision between the opinions and tastes of your friends
and acquaintances? Therefore, recommender systems are used to solve such
issues. The common algorithms use a similarity measure to predict active users'
tastes over a particular item. According to the cold start and data sparsity
problems, these systems cannot predict and suggest particular items to users.
In this paper, we introduce a new recommender system is able to find user
preferences and based on it, provides the recommendations. Our proposed system
called CUPCF is a combination of two similarity measures in collaborative
filtering to solve the data sparsity problem and poor prediction (high
prediction error rate) problems for better recommendation. The experimental
results based on MovieLens dataset show that, combined with the preferences of
the user's nearest neighbor, the proposed system error rate compared to a
number of state-of-the-art recommendation methods improved. Furthermore, the
results indicate the efficiency of CUPCF. The maximum improved error rate of
the system is 15.5% and the maximum values of Accuracy, Precision and Recall of
CUPCF are 0.91402, 0.91436 and 0.9974 respectively.
