  Knowledge graph (KG), which contains rich side information, becomes an
essential part to boost the recommendation performance and improve its
explainability. However, existing knowledge-aware recommendation methods
directly perform information propagation on KG and user-item bipartite graph,
ignoring the impacts of \textit{task-irrelevant knowledge propagation} and
\textit{vulnerability to interaction noise}, which limits their performance. To
solve these issues, we propose a robust knowledge-aware recommendation
framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune
the task-irrelevant knowledge associations and noisy implicit feedback
simultaneously. KRDN consists of an adaptive knowledge refining strategy and a
contrastive denoising mechanism, which are able to automatically distill
high-quality KG triplets for aggregation and prune noisy implicit feedback
respectively. Besides, we also design the self-adapted loss function and the
gradient estimator for model optimization. The experimental results on three
benchmark datasets demonstrate the effectiveness and robustness of KRDN over
the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and
also outperform robust recommendation models like SGL and SimGCL.
