Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Abstract

Recommender systems (RS) are an integral part of many online services aiming to provide an enhanced user-oriented experience. Machine learning (ML) models are nowadays broadly adopted in modern state-of-the-art approaches to recommendation, which are typically trained to maximize a user-centred utility (e.g., user satisfaction) or a business-oriented one (e.g., profitability or sales increase). They work under the main assumption that users' historical feedback can serve as proper ground-truth for model training and evaluation. However, driven by the success in the ML community, recent advances show that state-of-the-art recommendation approaches such as matrix factorization (MF) models or the ones based on deep neural networks can be vulnerable to adversarial perturbations applied on the input data. These adversarial samples can impede the ability for training high-quality MF models and can put the driven success of these approaches at high risk.

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