Recommender Systems Robust to Data Poisoning using Trim Learning

Abstract

Recommender systems have been widely utilized in various e-commerce systems for improving user experience. However, since security threats, such as fake reviews and fake ratings, are becoming apparent, users are beginning to have their doubts about trust of such systems. The data poisoning attack is one of representative attacks for recommender systems. While acting as a legitimate user on the system, the adversary attempts to manipulate recommended items using fake ratings. Although several defense methods also have been proposed, most of them require prior knowledge on real and/or fake ratings. We thus propose recommender systems robust to data poisoning without any knowledge.