Hybrid Recommendation System using Graph Neural Network and BERT
  Embeddings
Shashidhar Reddy Javaji Krutika Sarode
Recommender systems have emerged as a crucial component of the modern web
ecosystem. The effectiveness and accuracy of such systems are critical for
providing users with personalized recommendations that meet their specific
interests and needs. In this paper, we introduce a novel model that utilizes a
Graph Neural Network (GNN) in conjunction with sentence transformer embeddings
to predict anime recommendations for different users. Our model employs the
task of link prediction to create a recommendation system that considers both
the features of anime and user interactions with different anime. The
hybridization of the GNN and transformer embeddings enables us to capture both
inter-level and intra-level features of anime data.Our model not only
recommends anime to users but also predicts the rating a specific user would
give to an anime. We utilize the GraphSAGE network for model building and
weighted root mean square error (RMSE) to evaluate the performance of the
model. Our approach has the potential to significantly enhance the accuracy and
effectiveness of anime recommendation systems and can be extended to other
domains that require personalized recommendations.