Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design
This is an incremental position paper discussing ethical and social problems in recommendation systems for consumers and developers.
The paper examines how biases and attacks compromise fairness and privacy in recommendation systems, particularly highlighting the vulnerability of deep-learning collaborative filtering models to such issues.
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.