Toward a Knowledge-Augmented Recommender System: The Interplay Between User-Item Interaction and Knowledge Graph

Recommender systems struggle with data sparsity, but recent research suggests that incorporating high-quality side information is promising for improving performance.This paper proposes a novel knowledge-augmented recommender system that merges a user-item interaction graph with a knowledge graph.We explore relevant knowledge connections by attention mechanism, identify user-preferred item attributes through crossitem similarity scores, and use masked autoencoders to emphasize these connections and features further.Additionally, we cut off unreliable connections and ensure signal alignment between the graphs.Our experiments on Last-FM and MIND datasets demonstrate that our work outperforms existing recommender systems.