  Research about recommender systems emerges over the last decade and comprises
valuable services to increase different companies' revenue. Several approaches
exist in handling paper recommender systems. While most existing recommender
systems rely either on a content-based approach or a collaborative approach,
there are hybrid approaches that can improve recommendation accuracy using a
combination of both approaches. Even though many algorithms are proposed using
such methods, it is still necessary for further improvement. In this paper, we
propose a recommender system method using a graph-based model associated with
the similarity of users' ratings, in combination with users' demographic and
location information. By utilizing the advantages of Autoencoder feature
extraction, we extract new features based on all combined attributes. Using the
new set of features for clustering users, our proposed approach (GHRS) has
gained a significant improvement, which dominates other methods' performance in
the cold-start problem. The experimental results on the MovieLens dataset show
that the proposed algorithm outperforms many existing recommendation algorithms
on recommendation accuracy.
