Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems

2 December 2018

Papers citing "Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems"

11 / 11 papers shown

Title
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists Joachim Baumann Celestine Mendler-Dünner 78 2 0 17 Jan 2025
Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis Dacao Zhang Anton van den Hengel Le Wu Mi Tian Richang Hong Hao Wu 35 5 0 05 Jun 2024
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients Zihao Zhao Yi Jing Fuli Feng Jiancan Wu Chongming Gao Xiangnan He 24 9 0 26 Mar 2024
Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems Ashmi Banerjee Paromita Banik Wolfgang Wörndl 23 11 0 05 Sep 2023
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data Minghong Fang Jia-Wei Liu Michinari Momma Yi Sun 24 4 0 13 Dec 2022
Performative Reinforcement Learning Debmalya Mandal Stelios Triantafyllou Goran Radanović 30 17 0 30 Jun 2022
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems Lequn Wang Thorsten Joachims 19 22 0 30 May 2022
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings Cheng-Te Li Cheng-Mao Hsu Yang Zhang FaML 19 35 0 30 Apr 2022
TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers Yao Wu Jian Cao Guandong Xu Yudong Tan FaML 16 84 0 19 Apr 2021
Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy Bashir Rastegarpanah M. Crovella Krishna P. Gummadi FaML 21 8 0 19 May 2020
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility A. Chaney Brandon M Stewart Barbara E. Engelhardt CML 169 313 0 30 Oct 2017