  Recommender Systems are inevitable to personalize user's experiences on the
Internet. They are using different approaches to recommend the Top-K items to
users according to their preferences. Nowadays recommender systems have become
one of the most important parts of largescale data mining techniques. In this
paper, we propose a Hybrid Movie Recommender System (HMRS) based on Resource
Allocation to improve the accuracy of recommendation and solve the cold start
problem for a new movie. HMRS-RA uses a self-organizing mapping neural network
to clustering the users into N clusters. The users' preferences are different
according to their age and gender, therefore HMRS-RA is a combination of a
Content-Based Method for solving the cold start problem for a new movie and a
Collaborative Filtering model besides the demographic information of users. The
experimental results based on the MovieLens dataset show that the HMRS-RA
increases the accuracy of recommendation compared to the state-of-art and
similar works.
