Information theoretical analysis of unfair rating attacks under subjectivity - Wang, Dongxia (2021)
Ratings provided by advisors can help an advisee to make decisions, e.g., which seller to select in e-commerce. Unfair rating attacks - where dishonest ratings are provided to mislead the advisee - impact the accuracy of decision making. Current literature focuses on specific classes of unfair rating attacks, which does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behavior that is possible within a given system. We propose a probabilistic modeling of rating behavior, and apply information theory to quantitatively measure the impact of attacks. In particular, we can identify the attack with the worst impact. In the simple case, honest advisors report the truth straightforwardly, and attackers rate strategically. In real systems, the truth (or an advisor's view on it) may be subjective, making even honest ratings inaccurate. Although there exist methods to deal with subjective ratings, whether subjectivity influences the effect of unfair rating attacks was an open question. We discover that subjectivity decreases the robustness against attacks.