Fairness and Diversity in Recommender Systems: A Survey
Fuente:
arXiv
Saved in:
| Main Authors: | Zhao, Yuying, Wang, Yu, Liu, Yunchao, Cheng, Xueqi, Aggarwal, Charu, Derr, Tyler |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation
by: Zhao, Yuying, et al.
Published: (2024)
by: Zhao, Yuying, et al.
Published: (2024)
Edge Classification on Graphs: New Directions in Topological Imbalance
by: Cheng, Xueqi, et al.
Published: (2024)
by: Cheng, Xueqi, et al.
Published: (2024)
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
by: Zhao, Yuying, et al.
Published: (2024)
by: Zhao, Yuying, et al.
Published: (2024)
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
by: Zhao, Yuying, et al.
Published: (2025)
by: Zhao, Yuying, et al.
Published: (2025)
Causal Learning for Trustworthy Recommender Systems: A Survey
by: Li, Jin, et al.
Published: (2024)
by: Li, Jin, et al.
Published: (2024)
Knowledge Graph-based Session Recommendation with Adaptive Propagation
by: Wang, Yu, et al.
Published: (2024)
by: Wang, Yu, et al.
Published: (2024)
Robust Recommender System: A Survey and Future Directions
by: Zhang, Kaike, et al.
Published: (2023)
by: Zhang, Kaike, et al.
Published: (2023)
Augmenting Textual Generation via Topology Aware Retrieval
by: Wang, Yu, et al.
Published: (2024)
by: Wang, Yu, et al.
Published: (2024)
Towards Bridging Review Sparsity in Recommendation with Textual Edge Graph Representation
by: Wang, Leyao, et al.
Published: (2025)
by: Wang, Leyao, et al.
Published: (2025)
A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems
by: Zhao, Yu, et al.
Published: (2024)
by: Zhao, Yu, et al.
Published: (2024)
Personalized Denoising Implicit Feedback for Robust Recommender System
by: Zhang, Kaike, et al.
Published: (2025)
by: Zhang, Kaike, et al.
Published: (2025)
Embedding in Recommender Systems: A Survey
by: Wang, Maolin, et al.
Published: (2023)
by: Wang, Maolin, et al.
Published: (2023)
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
by: Zhang, Kaike, et al.
Published: (2024)
by: Zhang, Kaike, et al.
Published: (2024)
Poisoning Attacks against Recommender Systems: A Survey
by: Wang, Zongwei, et al.
Published: (2024)
by: Wang, Zongwei, et al.
Published: (2024)
Transparency, Privacy, and Fairness in Recommender Systems
by: Kowald, Dominik
Published: (2024)
by: Kowald, Dominik
Published: (2024)
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
by: Barenji, Samira Vaez, et al.
Published: (2025)
by: Barenji, Samira Vaez, et al.
Published: (2025)
A Survey on Trustworthy Recommender Systems
by: Ge, Yingqiang, et al.
Published: (2022)
by: Ge, Yingqiang, et al.
Published: (2022)
Multimodal Recommender Systems: A Survey
by: Liu, Qidong, et al.
Published: (2023)
by: Liu, Qidong, et al.
Published: (2023)
Enhancing New-item Fairness in Dynamic Recommender Systems
by: Guo, Huizhong, et al.
Published: (2025)
by: Guo, Huizhong, et al.
Published: (2025)
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval Algorithms
by: Xu, Chen, et al.
Published: (2025)
by: Xu, Chen, et al.
Published: (2025)
Measuring Fairness in Large-Scale Recommendation Systems with Missing Labels
by: Dong, Yulong, et al.
Published: (2024)
by: Dong, Yulong, et al.
Published: (2024)
Offline Evaluation Measures of Fairness in Recommender Systems
by: Rampisela, Theresia Veronika
Published: (2026)
by: Rampisela, Theresia Veronika
Published: (2026)
Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
by: Xia, Ziyuan, et al.
Published: (2022)
by: Xia, Ziyuan, et al.
Published: (2022)
Joint Modeling in Recommendations: A Survey
by: Zhao, Xiangyu, et al.
Published: (2025)
by: Zhao, Xiangyu, et al.
Published: (2025)
AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
by: Zhang, Kaike, et al.
Published: (2025)
by: Zhang, Kaike, et al.
Published: (2025)
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems
by: Vrijenhoek, Sanne, et al.
Published: (2024)
by: Vrijenhoek, Sanne, et al.
Published: (2024)
A Comprehensive Survey on Retrieval Methods in Recommender Systems
by: Huang, Junjie, et al.
Published: (2024)
by: Huang, Junjie, et al.
Published: (2024)
Towards Fair Large Language Model-based Recommender Systems without Costly Retraining
by: Li, Jin, et al.
Published: (2026)
by: Li, Jin, et al.
Published: (2026)
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
by: Rampisela, Theresia Veronika, et al.
Published: (2024)
by: Rampisela, Theresia Veronika, et al.
Published: (2024)
Data Augmentation for Sequential Recommendation: A Survey
by: Dang, Yizhou, et al.
Published: (2024)
by: Dang, Yizhou, et al.
Published: (2024)
A Survey on Diffusion Models for Recommender Systems
by: Lin, Jianghao, et al.
Published: (2024)
by: Lin, Jianghao, et al.
Published: (2024)
Learning to Hash for Recommendation: A Survey
by: Luo, Fangyuan, et al.
Published: (2024)
by: Luo, Fangyuan, et al.
Published: (2024)
Foundation Models for Recommender Systems: A Survey and New Perspectives
by: Huang, Chengkai, et al.
Published: (2024)
by: Huang, Chengkai, et al.
Published: (2024)
Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation
by: Zhang, Kaike, et al.
Published: (2024)
by: Zhang, Kaike, et al.
Published: (2024)
Augmenting Sequential Recommendation with Balanced Relevance and Diversity
by: Dang, Yizhou, et al.
Published: (2024)
by: Dang, Yizhou, et al.
Published: (2024)
Item-side Fairness of Large Language Model-based Recommendation System
by: Jiang, Meng, et al.
Published: (2024)
by: Jiang, Meng, et al.
Published: (2024)
Looking for Fairness in Recommender Systems
by: Logé, Cécile
Published: (2025)
by: Logé, Cécile
Published: (2025)
A Survey on Data-Centric Recommender Systems
by: Lai, Riwei, et al.
Published: (2024)
by: Lai, Riwei, et al.
Published: (2024)
On-Device Recommender Systems: A Comprehensive Survey
by: Yin, Hongzhi, et al.
Published: (2024)
by: Yin, Hongzhi, et al.
Published: (2024)
A Survey on Intent-aware Recommender Systems
by: Jannach, Dietmar, et al.
Published: (2024)
by: Jannach, Dietmar, et al.
Published: (2024)
Similar Items
-
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation
by: Zhao, Yuying, et al.
Published: (2024) -
Edge Classification on Graphs: New Directions in Topological Imbalance
by: Cheng, Xueqi, et al.
Published: (2024) -
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
by: Zhao, Yuying, et al.
Published: (2024) -
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
by: Zhao, Yuying, et al.
Published: (2025) -
Causal Learning for Trustworthy Recommender Systems: A Survey
by: Li, Jin, et al.
Published: (2024)