Hybrid Recommendation System using Graph Neural Network and BERT Embeddings

TL;DR

This paper presents a hybrid recommendation system combining Graph Neural Networks and BERT embeddings to improve anime recommendations by capturing complex features and predicting user ratings.

Contribution

Introduces a novel GNN and transformer-based hybrid model for personalized anime recommendations and rating prediction.

Findings

Improved recommendation accuracy demonstrated with weighted RMSE.

Effective capture of inter- and intra-level features of anime data.

Potential extension to other personalized recommendation domains.

Abstract

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.