Incentives for Item Duplication under Fair Ranking Policies

10/29/2021
by   Giorgio Maria Di Nunzio, et al.
0

Ranking is a fundamental operation in information access systems, to filter information and direct user attention towards items deemed most relevant to them. Due to position bias, items of similar relevance may receive significantly different exposure, raising fairness concerns for item providers and motivating recent research into fair ranking. While the area has progressed dramatically over recent years, no study to date has investigated the potential problem posed by duplicated items. Duplicates and near-duplicates are common in several domains, including marketplaces and document collections available to search engines. In this work, we study the behaviour of different fair ranking policies in the presence of duplicates, quantifying the extra-exposure gained by redundant items. We find that fairness-aware ranking policies may conflict with diversity, due to their potential to incentivize duplication more than policies solely focused on relevance. This fact poses a problem for system owners who, as a result of this incentive, may have to deal with increased redundancy, which is at odds with user satisfaction. Finally, we argue that this aspect represents a blind spot in the normative reasoning underlying common fair ranking metrics, as rewarding providers who duplicate their items with increased exposure seems unfair for the remaining providers.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/25/2022

Fairness of Exposure in Light of Incomplete Exposure Estimation

Fairness of exposure is a commonly used notion of fairness for ranking s...
research
12/21/2021

Understanding and Mitigating the Effect of Outliers in Fair Ranking

Traditional ranking systems are expected to sort items in the order of t...
research
12/18/2022

Marginal-Certainty-aware Fair Ranking Algorithm

Ranking systems are ubiquitous in modern Internet services, including on...
research
08/31/2021

Shallow pooling for sparse labels

Recent years have seen enormous gains in core IR tasks, including docume...
research
09/19/2023

Towards Measuring Fairness in Grid Layout in Recommender Systems

There has been significant research in the last five years on ensuring t...
research
04/27/2020

Evaluating Stochastic Rankings with Expected Exposure

We introduce the concept of expected exposure as the average attention r...
research
09/09/2018

Personalizing Fairness-aware Re-ranking

Personalized recommendation brings about novel challenges in ensuring fa...

Please sign up or login with your details

Forgot password? Click here to reset