Hybrid Recommendation System using Graph Neural Network and BERT Embeddings
This work addresses the need for more effective recommendation systems in the anime domain, with potential applications to other personalized recommendation areas, though it is incremental as it builds on existing GNN and transformer methods.
The paper tackles the problem of personalized anime recommendations by introducing a hybrid model that combines Graph Neural Networks (GNN) with BERT embeddings, achieving improved accuracy through link prediction and rating prediction tasks.
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.