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.

在这篇论文中，我们提出了一种名为SharpCF的简单而有效的方法，该方法在基本优化器上进行敌对训练的同时并不增加额外的计算成本。通过分析发现，敌对训练实际上是寻求模型参数位于最优模型参数周围，具有统一低损失值的领域，从而实现更好的普适性。为了减少计算开销，SharpCF引入了一种新的轨迹损失，用于衡量当前权重和过去权重之间的对齐程度。在真实世界数据集上的实验结果表明，与敌对训练相比，SharpCF在几乎不增加额外计算成本的情况下实现了更优异的性能。

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