User Oriented Fairness

User-oriented fairness in recommender systems focuses on ensuring that algorithms treat all user groups equitably, regardless of sensitive attributes like activity level or demographics, avoiding disparities in recommendation quality. Current research emphasizes mitigating biases during model training through techniques like constrained dominant sets and counterfactual fairness prompting, often incorporating these into existing recommendation models (e.g., collaborative filtering, deep learning architectures). Addressing this challenge is crucial for building trustworthy and inclusive recommender systems, impacting both the fairness of algorithmic outcomes and the overall user experience across various applications.

Papers

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 4, 2023

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li
Recommender System Recommendation Performance Fairness Loss User Oriented Fairness

August 29, 2023

Ensuring User-side Fairness in Dynamic Recommender Systems
Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong
Group Fairness Recommendation Fairness Fairness Loss Dynamic Recommender System User Oriented Fairness

June 12, 2023

Interpolating Item and User Fairness in Multi-Sided Recommendations
Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei, Djallel Bouneffouf
Interpolation Regime Different Stakeholder Multi Stakeholder Recommendation Fairness Online Platform User Oriented Fairness

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 16, 2023

Fairness-aware Differentially Private Collaborative Filtering
Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying
Differential Privacy Algorithmic Fairness Fairness Aware Differential Privacy Mechanism User Oriented Fairness

October 14, 2022

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel
Recommender System Real World Qualitative Difference Fermi Paradox Recommendation Fairness User Oriented Fairness

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

August 10, 2022

Fairness Based Energy-Efficient 3D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach
Nithin Babu, Igor Donevski, Alvaro Valcarce, Petar Popovski, Jimmy Jessen Nielsen, Constantinos B. Papadias
Procedural Fairness Unmanned Aerial Vehicle Deep Reinforcement Learning Approach Access Point User Oriented Fairness

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

February 27, 2022

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation
Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei
Recommender System Recommendation Model Point of Interest Specific Audience Popularity Bias User Oriented Fairness