MoRGH: movie recommender system using GNNs on heterogeneous graphs

Nowadays, with the advent of movies and TV shows and the competition between different movie streamer companies and movie databases to attract more users, movie recommenders have become a major prerequisite for customer satisfaction. Most of the previously introduced methods used collaborative, content-based, and hybrid filtering techniques, where neural network-based approaches and matrix completion are the major approaches of most recent movie recommender systems. The major drawbacks of previous systems are not considering side information, such as plot synopsis and cold start problem, in the context of movie recommendations. In this paper, we propose a novel inductive approach called MoRGH which first constructs a graph of similar movies by considering the information available in movies’ plot synopsis and genres. Second, we construct a heterogeneous graph that includes two types of nodes: movies and users. This graph is built using the MovieLens dataset and the similarity graph generated in the first stage, where each edge between a user and a movie represents the user’s rating for that movie, and each edge between two movies represents the similarity between them. Third, MoRGH mitigates the drawbacks of previous methods by employing a GNN and GAE-based model that combines collaborative and content-based approaches. This hybrid approach allows MoRGH to provide accurate and more personalized recommendations for each user, outperforming previous state-of-the-art models in terms of RMSE scores. The achieved improvement in RMSE scores demonstrates MoRGH’s superior performance and its ability to deliver enhanced recommendations compared to existing models.