Recommender systems that predict users' ratings for a set of fonts are known as a subset of information filtering systems. Movie recommendation systems are one of the most well-known and widely used systems that have been discussed in this research. Movie recommendation systems are efficient tools that help users find their favorite movies by checking users' previous interests. These systems are based on users' ratings of past movies and use them to predict their interests in the future. However, the inappropriate scoring that users provide leads to a problem called data sparsity. This problem reduces the efficiency of movie recommendation systems.On the other hand, other available data, such as the genre of movies and demographic information of users, play a vital role in helping recommender methods to generate better recommendations. This paper proposes a movie recommendation method using movie genres and users' demographic information. Also, we propose an efficient model to evaluate the user's scoring profile and determine the minimum score required to produce an accurate prediction.Then, suitable virtual scores are combined with profiles that have inappropriate scores. These virtual scores are calculated using similarity values ​​between users obtained from movie genres and users' demographic information. In addition, a useful criterion for determining the reliability of an item is introduced, which ensures the reliability of virtual scoring. Finally, unknown scores are predicted for the target user based on the developed scoring profiles. Experiments conducted on two famous movie recommendation datasets show that the proposed approach is more efficient than other compared recommenders.