Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design

TL;DR

This paper discusses the vulnerabilities of recommendation systems to malicious biases and attacks, emphasizing the importance of designing robust, fair, and privacy-preserving models to address ethical and social issues.

Contribution

It provides an analysis of attack types and biases in recommendation systems and highlights the necessity for developing defenses to enhance fairness and robustness.

Findings

Deep-learning collaborative filtering is highly vulnerable to bias.

Biases can lead to ethical and social issues.

Robust design is essential for fair and stable recommendation systems.

Abstract

Information has exploded on the Internet and mobile with the advent of the big data era. In particular, recommendation systems are widely used to help consumers who struggle to select the best products among such a large amount of information. However, recommendation systems are vulnerable to malicious user biases, such as fake reviews to promote or demote specific products, as well as attacks that steal personal information. Such biases and attacks compromise the fairness of the recommendation model and infringe the privacy of users and systems by distorting data.Recently, deep-learning collaborative filtering recommendation systems have shown to be more vulnerable to this bias. In this position paper, we examine the effects of bias that cause various ethical and social issues, and discuss the need for designing the robust recommendation system for fairness and stability.