DeepAI AI Chat
Log In Sign Up

Opportunistic Multi-aspect Fairness through Personalized Re-ranking

by   Nasim Sonboli, et al.
University of Colorado Boulder
DePaul University
The Chinese University of Hong Kong

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.


page 7

page 8


Fairness and Transparency in Recommendation: The Users' Perspective

Though recommender systems are defined by personalization, recent work h...

Personalizing Fairness-aware Re-ranking

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

Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework

Recommendation systems have illustrated the significant progress made in...

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bia...

Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems

Most literature in fairness has focused on improving fairness with respe...

BPMR: Bayesian Probabilistic Multivariate Ranking

Multi-aspect user preferences are attracting wider attention in recommen...

University of Washington at TREC 2020 Fairness Ranking Track

InfoSeeking Lab's FATE (Fairness Accountability Transparency Ethics) gro...