Movie Recommender System Based On Heterogeneous Graph Neural Networks

Heterogeneous Graph Neural Networks (GNNs) have shown good performance as a robust deep learning-based graph representation technique and have gained much research interest. Although it has adequately taken into account networks with a number of links and nodes, heterogeneity and the volume of semantic data provide significant obstacles. The attention mechanism, having great potential in a variety of areas, is one of the most interesting new developments in deep learning. This research demonstrates a system with two crucial attributes for embedding users and movies. The proposed framework achieves multi-level semantic attention using GNNs. We incorporated IMDB and Netflix Movie and TV Show datasets and merged them into a single consolidated dataset that was further utilized for results analysis. This paper mainly contributes a technique for movie recommendation using heterogeneous graphs and multi-level Semitics. We have proposed a framework that incorporates viewer and Director as an entity. During the research, we also combined two datasets in accordance with the proposed framework. After that, we evaluated the performance of the graph neural network on the heterogeneous graph. We discovered that the proposed model outperformed the current methodologies while using the proposed technique. Our model multilevel-Semitics-based framework shows effective results.