The idiosyncratic effects of adversarial training on bias in personalized recommendation learning

Abstract

Recently, recommendation systems have been proven to be susceptible to malicious perturbations of the model weights. To overcome this vulnerability, Adversarial Regularization emerged as one of the most effective solutions. Interestingly, the technique not only robustifies the model, but also significantly increases its accuracy. To date, unfortunately, the effect of Adversarial Regularization beyond-accuracy evaluation dimensions is unknown. This paper sheds light on these aspects and investigates how Adversarial Regularization impacts the amplification of popularity bias, and the deterioration of novelty and coverage of the recommendation list. The results highlight that, with imbalanced data distribution, Adversarial Regularization amplifies the popularity bias. Moreover, the empirical validation on five datasets confirms that it degrades the diversity and novelty of the generated recommendation. Code and data are available at https://github.com/sisinflab/The-Idiosyncratic-Effects-of-Adversarial-Training.