Reputation (In)dependence in Ranking Systems: Demographics Influence over Output Disparities

Boratto L.
2020-01-01

Abstract

Recent literature on ranking systems (RS) has considered users' exposure when they are the object of the ranking. Although items are the object of reputation-based RS, users have a central role also in this class of algorithms. Indeed, when ranking the items, user preferences are weighted by how relevant this user is in the platform (i.e., their reputation). In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences. We consider two demographic attributes, i.e., gender and age, and show that DR systematically occurs. Then, we propose mitigation, which ensures that reputation is independent of the users' sensitive attributes. Experiments on real-world data show that our approach can overcome DR and also improve ranking effectiveness.
2020
Inglese
SIGIR 2020: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
9781450380164
Association for Computing Machinery
2061
2064
4
43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020
Esperti anonimi
25-30 July 2020
Virtual, Online; China
internazionale
scientifica
Users; Demographic attributes; Ranking systems; Reputation
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Ramos, G.; Boratto, L.
273
2
4.1 Contributo in Atti di convegno
reserved
info:eu-repo/semantics/conferencePaper
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