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.

利用图神经网络（GNN）和句子转换器嵌入，我们介绍了一个新颖模型，用于预测不同用户的动漫推荐。我们的模型不仅向用户推荐动漫，还预测特定用户对动漫的评分。该模型的混合化使我们能够捕捉动漫数据的跨层级和层级内特征。我们采用GraphSAGE网络进行模型构建，并使用加权均方根误差（RMSE）评估模型的性能。我们的方法有潜力显著提高动漫推荐系统的准确性和效果，并可以推广到其他需要个性化推荐的领域。

基于图神经网络和BERT嵌入的混合推荐系统