{"total":75,"rows":[{"id":"2305.07609","title":"Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation","created":"2023-05-12","cited_by_count":114,"area":null},{"id":"2204.09888","title":"Fairness in Graph Mining: A Survey","created":"2022-04-21","cited_by_count":76,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2111.06061","title":"Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI","created":"2021-11-11","cited_by_count":39,"area":null},{"id":"2105.09829","title":"Personalized Counterfactual Fairness in Recommendation","created":"2021-05-20","cited_by_count":38,"area":null},{"id":"2305.12090","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","created":"2023-05-20","cited_by_count":38,"area":null},{"id":"2209.10117","title":"A Comprehensive Survey on Trustworthy Recommender Systems","created":"2022-09-21","cited_by_count":32,"area":null},{"id":"2205.11127","title":"Fairness in Recommender Systems: Research Landscape and Future Directions","created":"2022-05-23","cited_by_count":30,"area":null},{"id":"2204.08085","title":"CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems","created":"2022-04-17","cited_by_count":29,"area":null},{"id":"2401.10545","title":"Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency","created":"2024-01-19","cited_by_count":28,"area":null},{"id":"2205.13619","title":"Fairness in Recommendation: Foundations, Methods and Applications","created":"2022-05-26","cited_by_count":27,"area":null},{"id":"2308.15651","title":"Ensuring User-side Fairness in Dynamic Recommender Systems","created":"2023-08-29","cited_by_count":27,"area":null},{"id":"2206.03200","title":"FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning","created":"2022-06-07","cited_by_count":23,"area":null},{"id":"2502.14662","title":"iAgent: LLM Agent as a Shield between User and Recommender Systems","created":"2025-02-20","cited_by_count":23,"area":"agents"},{"id":"2206.04789","title":"Comprehensive Fair Meta-learned Recommender System","created":"2022-06-09","cited_by_count":18,"area":null},{"id":"2302.06333","title":"Improving Recommendation Fairness via Data Augmentation","created":"2023-02-13","cited_by_count":18,"area":null},{"id":"2306.00403","title":"A Survey on Fairness-aware Recommender Systems","created":"2023-06-01","cited_by_count":14,"area":null},{"id":"2205.08289","title":"Experiments on Generalizability of User-Oriented Fairness in Recommender Systems","created":"2022-05-17","cited_by_count":13,"area":null},{"id":"2310.04612","title":"A Topological Perspective on Demystifying GNN-Based Link Prediction Performance","created":"2023-10-06","cited_by_count":11,"area":null},{"id":"2407.18454","title":"Fairness Definitions in Language Models Explained","created":"2024-07-26","cited_by_count":11,"area":null},{"id":"2201.04399","title":"RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems","created":"2022-01-12","cited_by_count":10,"area":null},{"id":"2202.13307","title":"The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation","created":"2022-02-27","cited_by_count":9,"area":null},{"id":"2502.00690","title":"Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies","created":"2025-02-02","cited_by_count":9,"area":null},{"id":"2301.11535","title":"Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective","created":"2023-01-27","cited_by_count":8,"area":null},{"id":"2402.02816","title":"Intersectional Two-sided Fairness in Recommendation","created":"2024-02-05","cited_by_count":8,"area":null},{"id":"2410.17555","title":"FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning","created":"2024-10-23","cited_by_count":8,"area":null},{"id":"2408.14393","title":"CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence","created":"2024-08-26","cited_by_count":6,"area":"safety"},{"id":"2210.07770","title":"Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning","created":"2022-09-21","cited_by_count":4,"area":null},{"id":"2305.01801","title":"When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?","created":"2023-05-02","cited_by_count":4,"area":null},{"id":"2405.02219","title":"A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System","created":"2024-05-03","cited_by_count":4,"area":null},{"id":"2406.03064","title":"Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis","created":"2024-06-05","cited_by_count":4,"area":"lr"},{"id":"2304.07030","title":"FairRec: Fairness Testing for Deep Recommender Systems","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2306.10050","title":"Interpolating Item and User Fairness in Multi-Sided Recommendations","created":"2023-06-12","cited_by_count":3,"area":null},{"id":"2402.00485","title":"A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems","created":"2024-02-01","cited_by_count":3,"area":null},{"id":"2502.17921","title":"Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness","created":"2025-02-25","cited_by_count":3,"area":null},{"id":"2109.00177","title":"Problem Learning: Towards the Free Will of Machines","created":"2021-09-01","cited_by_count":2,"area":null},{"id":"2209.03904","title":"Analyzing the Effect of Sampling in GNNs on Individual Fairness","created":"2022-09-08","cited_by_count":2,"area":null},{"id":"2210.05936","title":"Equal Experience in Recommender Systems","created":"2022-10-12","cited_by_count":2,"area":null},{"id":"2304.09287","title":"Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search","created":"2023-04-18","cited_by_count":2,"area":null},{"id":"2305.14396","title":"FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software","created":"2023-05-23","cited_by_count":2,"area":null},{"id":"2307.13165","title":"Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations","created":"2023-07-24","cited_by_count":2,"area":null},{"id":"2309.11503","title":"Fairness Vs. Personalization: Towards Equity in Epistemic Utility","created":"2023-09-05","cited_by_count":2,"area":null},{"id":"2403.14681","title":"AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps","created":"2024-03-12","cited_by_count":2,"area":null},{"id":"2405.01063","title":"Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2405.13805","title":"Perceptual Fairness in Image Restoration","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2406.03052","title":"Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections","created":"2024-06-05","cited_by_count":2,"area":null},{"id":"2407.17459","title":"Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics","created":"2024-07-24","cited_by_count":2,"area":null},{"id":"2410.02791","title":"DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model","created":"2024-09-18","cited_by_count":2,"area":null},{"id":"2411.09847","title":"Towards a Fairer Non-negative Matrix Factorization","created":"2024-11-14","cited_by_count":2,"area":null},{"id":"2501.04762","title":"Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations","created":"2025-01-08","cited_by_count":2,"area":null},{"id":"2211.09072","title":"Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders","created":"2022-11-16","cited_by_count":1,"area":null},{"id":"2303.09527","title":"Fairness-aware Differentially Private Collaborative Filtering","created":"2023-03-16","cited_by_count":1,"area":null},{"id":"2305.15431","title":"Exploring and Exploiting Data Heterogeneity in Recommendation","created":"2023-05-21","cited_by_count":1,"area":null},{"id":"2309.01335","title":"In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems","created":"2023-09-04","cited_by_count":1,"area":null},{"id":"2309.15418","title":"Automatic Feature Fairness in Recommendation via Adversaries","created":"2023-09-27","cited_by_count":1,"area":null},{"id":"2312.03253","title":"Seller-side Outcome Fairness in Online Marketplaces","created":"2023-12-06","cited_by_count":1,"area":null},{"id":"2401.12593","title":"MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels","created":"2024-01-23","cited_by_count":1,"area":null},{"id":"2407.05625","title":"New User Event Prediction Through the Lens of Causal Inference","created":"2024-07-08","cited_by_count":1,"area":null},{"id":"2409.01605","title":"Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information","created":"2024-09-03","cited_by_count":1,"area":null},{"id":"2503.17231","title":"LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning","created":"2025-03-21","cited_by_count":1,"area":null},{"id":"2506.19777","title":"Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2209.04589","title":"Causal Intervention for Fairness in Multi-behavior Recommendation","created":"2022-09-10","cited_by_count":0,"area":null},{"id":"2211.01333","title":"Item-based Variational Auto-encoder for Fair Music Recommendation","created":"2022-10-24","cited_by_count":0,"area":null},{"id":"2302.10575","title":"Managing multi-facet bias in collaborative filtering recommender systems","created":"2023-02-21","cited_by_count":0,"area":null},{"id":"2308.02898","title":"Elucidate Gender Fairness in Singing Voice Transcription","created":"2023-08-05","cited_by_count":0,"area":null},{"id":"2404.08887","title":"Countering Mainstream Bias via End-to-End Adaptive Local Learning","created":"2024-04-13","cited_by_count":0,"area":null},{"id":"2405.15788","title":"Towards Fairness in Provably Communication-Efficient Federated Recommender Systems","created":"2024-05-03","cited_by_count":0,"area":null},{"id":"2504.07108","title":"OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2505.19441","title":"Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2506.18327","title":"Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems","created":"2025-06-23","cited_by_count":0,"area":null},{"id":"2506.20329","title":"Producer-Fairness in Sequential Bundle Recommendation","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2512.21348","title":"Fairness Is Not Just Ethical: Performance Trade-Off via Data Correlation Tuning to Mitigate Bias in ML Software","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2601.20848","title":"Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2603.13253","title":"A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2606.29064","title":"Fairness Attacks on Recommender Systems","created":"2026-06-27","cited_by_count":0,"area":null}],"next_cursor":null}