Adversarial Collaborative Filtering for Free
Huiyuan Chen Xiaoting Li Vivian Lai Chin-Chia Michael Yeh Yujie Fan Yan Zheng Mahashweta Das Hao Yang
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.