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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., protability or sales increase). They work under the main assumption that users’ histor- ical feedback can serve as proper ground-truth for model training and evaluation. However, driven by the success in the ML commu- nity, 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. As a result, there is a new paradigm of secure training for RS that takes into ac- count the presence of adversarial samples into the recommendation process.

We present adversarial machine learning in Recommender Systems (AML-RecSys), which concerns the study of eective ML techniques in RS to ght against an adversarial component. AML-RecSys has been proposed in two main fashions within the RS literature: (i) adversarial regularization, which attempts to combat against ad- versarial perturbation added to input data or model parameters of a RS and, (ii) generative adversarial network (GAN)-based models, which adopt a generative process to train powerful ML models. We discuss a theoretical framework to unify the two above models, which is performed via a minimax game between an adversarial component and a discriminator. Furthermore, we explore various examples illustrating the successful application of AML to solve various RS tasks. Finally, we present a global taxonomy/overview of the academic literature based on several identied dimensions, namely (i) research goals and challenges, (ii) application domains and (iii) technical overview.