Statistical Filtering for Fair Item Ranking

Online item ranking systems are crucial for digital marketplaces, directly influencing user experience and vendor revenue. Traditional reputation-based ranking systems weight ratings according to user reputation scores. They have proven effective against manipulation but raise significant ethical concerns regarding user discrimination and privacy, and may raise concerns under emerging regulatory frameworks in certain application contexts. While a user-agnostic ranking system was recently introduced as an alternative approach that uses statistical filtering instead of user reputation scores, its theoretical foundations and resistance to bribing strategies remained unexplored. In this paper, we provide the first comprehensive theoretical analysis of user-agnostic ranking system's robustness properties. We establish formal bounds on bribing resistance by proving three key properties: strategy composition conditions, profitability constraints, and statistical validity requirements. Our theoretical framework demonstrates that profitable bribing strategies in this class of system must satisfy strict statistical conditions, making manipulation more difficult than in reputation-based systems. Experimental evaluation on three real-world datasets confirms our theoretical findings, showing that user-agnostic ranking systems can achieve superior bribing resistance while maintaining comparable effectiveness and efficiency.