Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negatively impacts the quality of recommendation. To tackle this problem, many prior studies leverage adversarial learning to regularize the representations of users/items, which improves both generalizability and robustness. Those methods often learn adversarial perturbations and model parameters under min-max optimization framework. However, there still have two major drawbacks: 1) Existing methods lack theoretical guarantees of why adding perturbations improve the model generalizability and robustness; 2) Solving min-max optimization is time-consuming. In addition to updating the model parameters, each iteration requires additional computations to update the perturbations, making them not scalable for industry-scale datasets. In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer. To achieve this goal, we first revisit the existing adversarial collaborative filtering and discuss its connection with recent Sharpness-aware Minimization. This analysis shows that adversarial training actually seeks model parameters that lie in neighborhoods around the optimal model parameters having uniformly low loss values, resulting in better generalizability. To reduce the computational overhead, SharpCF introduces a novel trajectory loss to measure the alignment between current weights and past weights. Experimental results on real-world datasets demonstrate that our SharpCF achieves superior performance with almost zero additional computational cost comparing to adversarial training.

翻译：协同过滤（Collaborative Filtering, CF）已被成功用于帮助用户发现感兴趣的项目。然而，现有CF方法受到噪声数据问题的影响，这降低了推荐质量。为解决此问题，许多先前研究利用对抗学习来正则化用户/项目的表示，从而提升泛化性和鲁棒性。这些方法通常在最小-最大优化框架下学习对抗扰动和模型参数。但存在两个主要缺陷：1）现有方法缺乏理论保证来解释为何添加扰动能提升模型泛化性和鲁棒性；2）求解最小-最大优化耗时巨大。除更新模型参数外，每次迭代需要额外计算来更新扰动，使其难以扩展至工业级数据集。本文提出锐度感知协同过滤（Sharpness-aware Collaborative Filtering, SharpCF）——一种简单而有效的方法，能在不增加基础优化器额外计算成本的情况下执行对抗训练。为实现此目标，我们首先重新审视现有对抗协同过滤方法，并讨论其与近期锐度感知最小化方法的联系。分析表明，对抗训练实际上是在寻找位于最优模型参数邻域内、具有均匀低损失值的参数，从而获得更好的泛化性。为降低计算开销，SharpCF引入新颖的轨迹损失来衡量当前权重与历史权重的对齐程度。真实数据集上的实验结果表明，相较于对抗训练，我们的SharpCF能以几乎为零的额外计算成本实现卓越性能。