research-article

A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks

Authors:

Yashar Deldjoo,

Tommaso Di Noia,

Felice Antonio MerraAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 2

Article No.: 35, Pages 1 - 38

https://doi.org/10.1145/3439729

Published: 05 March 2021 Publication History

Abstract

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: Many applications of machine learning (ML) are adversarial in nature [146]. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs.

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models) and (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community working on the security of RS or on generative models using GANs to improve their quality.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 2

March 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3450359

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University of Sydney, Australia

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Publication History

Published: 05 March 2021

Accepted: 01 November 2020

Revised: 01 November 2020

Received: 01 May 2020

Published in CSUR Volume 54, Issue 2

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