The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.

翻译：在现实推荐系统中，显式用户反馈（如评分）的获取往往受限于需要用户主动参与。为缓解这一问题，用户浏览过程中产生的隐式反馈（如点击）被用作可行的替代方案。然而，隐式反馈存在高度噪声，这会显著降低推荐质量。尽管已有许多方法通过为隐式反馈分配不同权重来解决该问题，但仍存在两个不足：（1）这些方法中的权重计算与迭代无关，未考虑前序迭代中权重的影响；（2）权重计算常依赖先验知识，而先验知识并非总是可用或具有普适性。为克服这两个局限，我们将推荐去噪建模为一个双层优化问题。内层优化旨在推导有效的推荐模型，并引导权重的确定，从而消除对先验知识的依赖。外层优化利用内层优化的梯度，并以考虑前序权重影响的方式调整权重。为高效求解该双层优化问题，我们采用权重生成器以避免权重的存储，并基于一步梯度匹配的损失函数显著降低计算时间。在三个基准数据集上的实验结果表明，我们提出的方法优于当前最先进的通用推荐模型和去噪推荐模型。代码已在 https://github.com/CoderWZW/BOD 公开。