Keyword-enhanced recommender system based on inductive graph matrix completion

Going beyond the user-item rating information, recent studies have utilized additional information to improve the performance of recommender systems. Graph neural network (GNN) based approaches are among the most common. However, existing models that utilize text data require a lot of computing resources and have a complex structure that makes them difficult to utilize in real -world applications. In this research, we propose a new method, keyword -enhanced graph matrix completion (KGMC), which utilizes keyword sharing relationships in user-item graphs. Our model has a simpler structure and requires less computing resources than existing models that utilize text data, but it has the advantage of cross -domain transferability while providing an intuitive understanding of the inference results. KGMC consists of three steps: (1) keyword extraction from the review text, (2) subgraph extraction and keyword -enhanced subgraph construction, and (3) GNN-based rating prediction. We have conducted extensive experiments over eight benchmark datasets examine the relative superiority of the proposed KGMC method, compared to state-of-the-art baselines. Additional experiments and case studies have been also conducted to demonstrate the transferability as well keyword -based explainability of KGMC. Our findings highlight the practical advantages of our model for recommender systems and support its effectiveness in inductive graph -based link prediction.