Fairness in Recommender Systems

Latest papers

15 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models
Sep 29, 2026Song-Li Wu, Xianquan Wang, Zhaocheng Du +2Diffusion Guidance Algorithmic Fairness
Tsinghua University · University of Science and Technology of China · Huawei Noah’s Ark Lab
FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation
Aug 13, 2026Yuchen Zheng, Sihan Xu, Jingwen Yang +3Algorithmic Fairness Fairness in Recommender Systems
1VCIP, TMCC, DISSec, College of Computer Science, Nankai University · 2VCIP, DISSec, College of Cryptology and Cyber Science, Nankai University
Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders
Aug 8, 2026Chan Aristella Lu, Arya Fayyazi, Junhao Zhang +6LLM Auditing Language Model Bias Evaluation
University of Georgia · University of Southern California · Carnegie Mellon University +1
Fairness Attacks on Recommender Systems
Jun 27, 2026Yanan Wang, Yong GeAdversarial Attacks Algorithmic Fairness
Department of Information Systems and Operations Management, College of Business, The University of Texas at Arlington · Department of Management Information Systems, Eller College of Management, the University of Arizona
Reproducing FACTER: Fairness via Conformal Thresholding and Prompt Repair
Jun 26, 2026Oscar Miró López-Feliu, Daimy van Loo, Xanthos Kekkos +2Algorithmic Fairness Conformal Prediction
University of Amsterdam
Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits
Apr 9, 2026Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshminarayanan Subramanian +1Multi-Agent Bandits Recommender Systems
Courant Institute, New York University · Indian Institute of Technology Bombay · FAIR, Meta AI
Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty
Apr 2, 2026Enock O. Ayiku, Evelyn Osei, Emebo OnyekaRecommender Systems Fairness in Recommender Systems
University of Massachusetts Boston, Boston, MA, USA · Kansas State University, Manhattan, KS, USA · Virginia Tech, Blacksburg, VA, USA
Exploring How Fair Model Representations Relate to Fair Recommendations
Mar 25, 2026Bjørnar Vassøy, Benjamin Kille, Helge LangsethFairness in Recommender Systems Algorithmic Fairness
Norwegian University of Science and Technology, Trondheim, Norway
Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters
Mar 24, 2026Nan Cui, Wendy Hui Wang, Yue NingFairness in Recommender Systems Demographic Bias in Language Models
Department of Computer Science, Stevens Institute of Technology, Hoboken NJ, USA
Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation
Mar 12, 2026Uttamasha Anjally Oyshi, Susan GauchFairness in Recommender Systems Algorithmic Fairness
Department of Electrical Engineering & Computer Science, University of Arkansas, Fayetteville, USA
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Mar 4, 2026Yun Lu, Xiaoyu Shi, Hong Xie +2Fairness in Recommender Systems Recommender Systems
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences Chongqing, China · The First Affiliated Hospital, University of Science and Technology of China Hefei, China · City University of Hong Kong Hong Kong, Hong Kong
Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
Mar 3, 2026Chongjun Xia, Xiaoyu Shi, Hong Xie +3Long-Tail Recommendation Fairness in Recommender Systems
Chinese Academy of Sciences · University of Technology Sydney · University of Science and Technology of China
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
Feb 25, 2026Uttamasha Anjally Oyshi, Susan GauchFairness in Recommender Systems Recommender Systems
Department of Electrical Engineering & Computer Science University of Arkansas Fayetteville, USA
A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System
Feb 24, 2026Nikita Baidya, Bidyut Kr. Patra, Ratnakar DashFairness in Recommender Systems Recommender Systems
Pattern Recognition Laboratory, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India. · Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh, India.
Fairness Dynamics in Digital Economy Platforms with Biased Ratings
Feb 18, 2026J. Martin Smit, Fernando P. SantosFairness in Recommender Systems Algorithmic Fairness
University of Amsterdam, Amsterdam, Netherlands

Sep 29, 2026J/K move · Enter open · S save

FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models

Song-Li Wu, Xianquan Wang, Zhaocheng Du, Weinan Gan+1

Tsinghua University · University of Science and Technology of China · Huawei Noah’s Ark Lab

Diffusion Guidance Algorithmic Fairness Recommender Systems Popularity Bias in Recommender Systems Fairness in Recommender Systems

Open paper AskPDF arXiv

While the "Matthew Effect" and filter bubbles are widely recognized outcome-level biases in recommender systems, we reveal that Diffusion Recommender Models (DRMs) uniquely compound this issue through their generative dynamics. Rather than merely inheriting data imbalances, DRMs trigger a self-reinforcing amplification of popularity bias. We identify that this phenomenon is driven by two compounding mechanisms. First, while optimization loss is universally dominated by high-frequency items across recommenders, DRMs suffer from a unique structural prior mismatch during generation. Because the forward terminal distribution of long-tailed data deviates significantly from the standard Gaussian prior, reverse sampling trajectories inherently collapse toward high-density popular items, fundamentally suppressing niche item generation. To dismantle this self-reinforcing loop, we propose FairDiff, a plug-and-play fairness-aware diffusion framework. To overcome the popularity-dominated loss, we introduce Popularity Condition Guidance (PCG). Rather than altering the training objective, PCG acts as an inference-time distributional reweighting mechanism, mathematically reshaping the score-based gradient field to penalize high-popularity regions and guide trajectories toward niche semantics. Furthermore, we design a Semantic Calibration (SC) Module to bridge the prior mismatch, aligning the forward and reverse distributions via one-step optimal transport. Comprehensive evaluations demonstrate that FairDiff achieves state-of-the-art performance while effectively mitigating the self-reinforcing Matthew Effect, highlighting its value as a general framework for DRMs.