MoRGH: Movie Recommender System using GNNs on Heterogeneous Graphs

Abstract

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, cold start problem, and neglecting valuable information hidden when the data is presented in a heterogeneous graph like movie-movie or user-user information. 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, constructs a heterogeneous graph of users and movies by taking into account the movies’ rating information available in the Movielens dataset. Third, mitigates the drawbacks of previous methods by performing a GNN and GAE-based model to exploit a hybrid approach of collaborative and content-based approaches. Finally recommends, accurate and more personalized recommendations for each user, which outperforms previous state-of-the-art models w.r.t RMSE scores with an average of 0.1 based on movie ratings.