Centralized item ranking in online marketplaces compromises user privacy and is vulnerable to manipulation. The introduction of a federated, reputation-based ranking system preserves privacy, ensures fairness, and delivers robust and effective rankings. The growth of online marketplaces has transformed consumer experiences, offering diverse products aggregated from multiple sellers. However, the centralized nature of these platforms …
Category: Ranking systems
Towards Ethical Item Ranking: A Paradigm Shift from User-Centric to Item-Centric Approaches
By eliminating user-centric biases and adopting a purely item-focused approach, it is possible to achieve ethical and effective ranking systems—ensuring fairness, resilience, and compliance with regulations on responsible AI. Ranking systems are essential in online platforms, shaping user experiences and influencing product visibility and sales. However, traditional user-centric ranking systems, which assign reputation scores to …
Robust reputation independence in ranking systems for multiple sensitive attributes
Ranking systems that account for the reputation of the users can be biased towards different demographic groups, especially when considering multiple sensitive attributes (e.g., gender and age). Providing guarantees of reputation independence can lead to unbiased and effective rankings. Moreover, these rankings are also robust to attacks. In a study, published by the Machine Learning …
Reputation Equity in Ranking Systems
Reputation-based ranking systems can be biased towards the sensitive attributes of the users, meaning that certain demographic groups have systematically lower reputation scores. Nevertheless, if we unbias the reputation scores considering one sensitive attribute, bias still occurs when considering different sensitive attributes. For this reason, reputation scores should be unbiased independently of any sensitive attribute …
A Robust Reputation-based Group Ranking System and its Resistance to Bribery
Non-personalized ranking systems that average the ratings of individual users are prone to attacks associated with bribing. Grouping users according to their preferences and weighting the average by the reputation of the users allows to generate more personalized rankings. These rankings are also less prone to attacks. In a paper published in the ACM Transactions …
Reputation (in)dependence in ranking systems: demographics influence over output disparities
Your reputation on the Web does not depend only on your behavior, but also on your sensitive attributes. Concretely, belonging to a minority demographic group affects your reputation and how your preferences are valued in online ranking systems. In a recent SIGIR 2020 paper with Guilherme Ramos, we considered reputation-based ranking systems, which is a …