Matching Consumer Fairness Objectives Strategies for RecSys

09/06/2022
by   Michael D. Ekstrand, et al.
0

The last several years have brought a growing body of work on ensuring that recommender systems are in some sense consumer-fair – that is, they provide comparable quality of service, accuracy of representation, and other effects to their users. However, there are many different strategies to make systems more fair and a range of intervention points. In this position paper, we build on ongoing work to highlight the need for researchers and practitioners to attend to the details of their application, users, and the fairness objective they aim to achieve, and adopt interventions that are appropriate to the situation. We argue that consumer fairness should be a creative endeavor flowing from the particularities of the specific problem to be solved.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/08/2022

Who Pays? Personalization, Bossiness and the Cost of Fairness

Fairness-aware recommender systems that have a provider-side fairness co...
research
05/16/2023

Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation

In the current landscape of ever-increasing levels of digitalization, we...
research
04/17/2022

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Recently, there has been a rising awareness that when machine learning (...
research
09/05/2023

Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems

This position paper summarizes our published review on individual and mu...
research
01/21/2022

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Enabling non-discrimination for end-users of recommender systems by intr...
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
04/13/2021

Fairness in Rankings and Recommendations: An Overview

We increasingly depend on a variety of data-driven algorithmic systems t...

Please sign up or login with your details

Forgot password? Click here to reset