Fairness Aware Recommendation

Fairness-aware recommendation aims to mitigate biases in recommender systems that lead to unfair treatment of different user groups based on sensitive attributes like gender or age. Current research focuses on developing methods to address various facets of fairness, including user-side, item-side, and intersectional fairness, employing techniques such as multi-agent social choice mechanisms, adversarial debiasing, data augmentation, and multi-task learning with graph embeddings. These advancements are crucial for ensuring equitable access to information and opportunities, impacting both the ethical development of recommender systems and their real-world applications in areas like job recruitment and product marketing.

Papers

October 23, 2024

FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
Wei Chen, Meng Yuan, Zhao Zhang, Ruobing Xie, Fuzhen Zhuang, Deqing Wang, Rui Liu
LeArning Abstract Contrastive Learning Contrastive Objective Adversarial Contrastive Learning Recommendation Fairness Fairness Aware Recommendation Temporal Contrastive Graph

February 5, 2024

Intersectional Two-sided Fairness in Recommendation
Yifan Wang, Peijie Sun, Weizhi Ma, Min Zhang, Yuan Zhang, Peng Jiang, Shaoping Ma
Effective Recommendation Intersectional Fairness User Oriented Fairness Fairness Aware Recommendation

September 10, 2023

Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
Amanda Aird, Cassidy All, Paresha Farastu, Elena Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke
Fairness Evaluation Social Choice Balancing Fairness Recommendation Fairness Fairness Aware Recommendation

May 20, 2023

UP5: Unbiased Foundation Model for Fairness-aware Recommendation
Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang
Recommender System Recommendation Fairness User Oriented Fairness Fairness Aware Recommendation LLM Based Recommendation

March 2, 2023

Dynamic fairness-aware recommendation through multi-agent social choice
Amanda Aird, Paresha Farastu, Joshua Sun, Elena Štefancová, Cassidy All, Amy Voida, Nicholas Mattei, Robin Burke
Personalized Recommendation Multi Agent Decision Fairness Issue Recommendation Fairness Fairness Aware Recommendation

February 13, 2023

Improving Recommendation Fairness via Data Augmentation
Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang
Data Augmentation Collaborative Filtering Recommendation Model Data Balancing Recommendation Fairness Fairness Aware Recommendation Fairness Optimization

September 20, 2022

Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation
Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker
Algorithmic Bias Fair Representation Job Recommendation Adversarial Debiasing Fair Adversarial Fairness Aware Recommendation

September 8, 2022

Who Pays? Personalization, Bossiness and the Cost of Fairness
Paresha Farastu, Nicholas Mattei, Robin Burke
Procedural Fairness Hidden CoST Fairness Objective Dominance Hierarchy User Oriented Fairness Fairness Aware Recommendation Fairness Aware Recommender System

May 17, 2022

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems
Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi
Recommender System Stronger Generalizability Optical Experiment User Oriented Fairness Fairness Aware Recommendation

April 30, 2022

FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings
Cheng-Te Li, Cheng Hsu, Yang Zhang
Multi Task Learning Sequential Recommendation Fairness Intervention Preference Representation Recommendation Fairness Fairness Aware Graph Fairness Aware Recommendation