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.

翻译：知识图谱（KG）包含丰富的辅助信息，已成为提升推荐性能及其可解释性的关键组成部分。然而，现有知识感知推荐方法直接在知识图谱和用户-物品二分图上进行信息传播，忽视了"任务无关知识传播"与"对交互噪声的脆弱性"的影响，从而限制了其性能。为解决这些问题，我们提出了一种鲁棒的知识感知推荐框架——知识精炼去噪网络（KRDN），可同时剪枝任务无关的知识关联和噪声隐式反馈。KRDN由自适应知识精炼策略和对比去噪机制组成，能够分别自动蒸馏高质量的知识图谱三元组用于聚合，并剪枝噪声隐式反馈。此外，我们还设计了自适应损失函数和梯度估计器用于模型优化。在三个基准数据集上的实验结果表明，KRDN在有效性和鲁棒性上均优于KGIN、MCCLK、KGCL等最先进的知识感知方法，同时也超越了SGL、SimGCL等鲁棒推荐模型。