Low-rank Defenses Against Adversarial Attacks in Recommender Systems.

Recommender systems are powerful tools which touch on numerous aspects of everyday life, from shopping to consuming content, and beyond. However, as other machine learning models, recommender system models are vulnerable to adversarial attacks and their performance could drop significantly with a slight modification of the input data. Most of the studies in the area of adversarial machine learning are focused on the image and vision domain. There are very few work that study adversarial attacks on recommender systems and even fewer work that study ways to make the recommender systems robust and reliable. In this study, we explore two state-of-the-art adversarial attack methods proposed by Tang et al. [1] and Christakopoulou et al. [2] and we report our proposed defenses and experimental evaluations against these attacks. In particular, we observe that low-rank reconstructions and/or transformation of the attacked data has a significant alleviating effect on the attack, and we present extensive experimental evidence to demonstrate the effectiveness of this approach. We also show that a simple classifier is able to learn to detect fake users from real users and can successfully discard them from the dataset. This observation elaborates the fact that the threat model does not generate fake users that mimic the same behavior of real users and can be easily distinguished from real users’ behavior. We also examine how transforming latent factors of the matrix factorization model into a low-dimensional space impacts its performance. Furthermore, we combine fake users from both attacks to examine how our proposed defense is able to defend against multiple attacks at the same time. Local low-rank reconstruction was able to reduce the hit ratio of target items from 23.54% to 15.69% while the overall performance of the recommender system was preserved.Adversarial machine learning, recommender systems, low-rank reconstruction