Abstract: Knowledge graph-based recommendation models achieve accurate user preference modeling by capturing entity associations of interaction items on the knowledge graph,thereby enhancing recommendation accuracy.However,existing research ignores the noise and sparsity issues in the interaction graph,which limits the model's ability to capture entity associations and leads to biased,ultimately leading to suboptimal results.To address these issues,this paper proposes a model named knowledge-aware graph refinement network(KGRN).Specifically,a graph pruning module is designed that utilizes semantic information from the knowledge graph to dynamically prune noisy interactions in the interaction graph.Additionally,a graph construction module is developed to mitigate data sparsity in the interaction graph,enhance the model's capability to identify user preference entities,and improve user preference modeling.Comparative experiments are conducted on three benchmark datasets to evaluate the effectiveness of KGRN.Compared to existing models,KGRN achieves performance improvements of 2.97% on MovieLens-1M,1.69% on Amazon-Book,and 2.22% on BookCrossing,demonstrating the effectiveness of the proposed model.

Key words: Graph neural networks, Knowledge graphs, Feature Learning, Recommendation systems, Feature fusion