This repository aims to provide comprehensive paper lists of work on fairness and diversity in recommender systems.
Fairness in Recommendation: A Survey Li, Yunqi, et al. arXiv preprint arXiv:2205.13619 (2022). [Paper]
A Survey on the Fairness of Recommender Systems Wang, Yifan, et al ACM Transactions on Information Systems 41.3 (2023)[Paper]
A Survey of Research on Fair Recommender Systems Deldjoo, Yashar, et al. arXiv preprint arXiv:2205.11127 (2022). [Paper]
Bias and Debias in Recommender Systems-A Survey and Future Direction Chen et al. [Paper] [Code]
Fairness in rankings and recommendations: an overview Pitoura, Evaggelia, Kostas Stefanidis, and Georgia Koutrika. The VLDB Journal (2022)[Paper]
Diversity in Recommender Systems - A Survey. Kunaver, Matevž, and Tomaž Požrl. Knowledge-based systems 2017. [Paper]
Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. Kaminskas, Marius, and Derek Bridge. ACM Transactions on Interactive Intelligent Systems (TiiS) 7.1 (2016): 1-42. [Paper]
Novelty and Diversity in Recommender Systems Castells, Pablo, Neil Hurley, and Saul Vargas. Springer US, 2021.[Paper]
Recent Advances in Diversified Recommendation Wu, Qiong, et al. arXiv preprint arXiv:1905.06589 (2019). [Paper]
A Survey of Diversification Techniques in Search and Recommendation Wu, Haolun, et al. arXiv preprint arXiv:2212.14464 (2022). [Paper]
A summary table of fairness for user diversity is as follows:
Explict
Gender
A flexible framework for evaluating user and item fairness in recommender systems Deldjoo, Yashar, et al. User Modeling and User-Adapted Interaction (2021): 1-55. [Paper]
Fairness-aware news recommendation with decomposed adversarial learning Wu, Chuhan, et al. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 5. 2021. [Paper]
Race
On the problem of underranking in group-fair ranking Gorantla, Sruthi, Amit Deshpande, and Anand Louis. International Conference on Machine Learning. PMLR, 2021. [Paper]
Fairness-aware tensor-based recommendation Zhu, Ziwei, Xia Hu, and James Caverlee. Proceedings of the 27th ACM international conference on information and knowledge management. 2018. [Paper]
Age
A fairness-aware hybrid recommender system Farnadi, Golnoosh, et al. arXiv preprint arXiv:1809.09030 (2018). [Paper]
Implict (Behavior)
User-oriented fairness in recommendation Li, Yunqi, et al. Proceedings of the Web Conference 2021. 2021. [Paper]
Pareto optimality for fairness-constrained collaborative filtering Hao, Qianxiu, et al. Proceedings of the 29th ACM International Conference on Multimedia. 2021. [Paper]
Fairness-aware explainable recommendation over knowledge graphs Fu, Zuohui, et al. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. [Paper]
An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness Xiao, Yang, et al. Journal of Network and Computer Applications 156 (2020). [Paper]
Are Big Recommendation Models Fair to Cold Users? Wu, Chuhan, et al. arXiv preprint arXiv:2202.13607 (2022). [Paper]
Calibrated Recommendation Steck, Harald. Proceedings of the 12th ACM conference on recommender systems. 2018. [Paper]
The unfairness of popularity bias in recommendation Abdollahpouri, Himan, et al. arXiv preprint arXiv:1907.13286 (2019). [Paper]
User-centered evaluation of popularity bias in recommender systems Abdollahpouri, Himan, et al. Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. 2021. [Paper]
Exploiting personalized calibration and metrics for fairness recommendation da Silva, Diego Corrêa, Marcelo Garcia Manzato, and Frederico Araújo Durão. Expert Systems with Applications 181 (2021): 115112. [Paper]
rabbit holes and taste distortion: distribution-aware recommendation with evolving interests Zhao, Xing, Ziwei Zhu, and James Caverlee. Proceedings of the Web Conference 2021. 2021. [Paper]
Personalizing Fairness-aware Re-ranking Liu, Weiwen, and Robin Burke. arXiv preprint arXiv:1809.02921 (2018). [Paper]
Personalized Fairness-aware Re-ranking for Microlending Liu, Weiwen, et al. Proceedings of the 13th ACM conference on recommender systems. 2019. [Paper]
Opportunistic Multi-aspect Fairness through Personalized Re-ranking Sonboli, Nasim, et al. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020. [Paper]
Selective Fairness in Recommendation via Prompts Wu, Yiqing, et al. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022. [Paper] [Code]
Towards Personalized Fairness based on Causal Notion Li, Yunqi, et al. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. [Paper] [Code]
User-controllable Recommendation Against Filter Bubbles Wang, Wenjie, et al. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022. [Paper] [Code]
Controllable Universal Fair Representation Learning Cui, Yue, et al. Proceedings of the ACM Web Conference 2023. 2023. [Paper]
Multi-interest network with dynamic routing for recommendation at Tmall Li, Chao, et al. Proceedings of the 28th ACM international conference on information and knowledge management. 2019. [Paper]
Controllable multi-interest framework for recommendation Cen, Yukuo, et al. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020. [Paper] [Code]
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest Pal, Aditya, et al. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020. [Paper]
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation Zhang, Shengyu, et al. Proceedings of the ACM Web Conference 2022. 2022. [Paper][Code]
everyone’s preference changes differently: weighted multi-interest retrieval model Shi, Hui, et al. (2023). [Paper]
Disentangled Graph Convolutional Networks Ma, Jianxin, et al. International conference on machine learning. PMLR, 2019. [Paper] [Code]
Disentangled Graph Collaborative Filtering Wang, Xiang, et al. Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020. [Paper] [Code]
Learning disentangled representations for recommendation Ma, Jianxin, et al. Advances in neural information processing systems 32 (2019). [Paper] [Code]
Personalizing Fairness-aware Re-ranking Liu, Weiwen, and Robin Burke. arXiv preprint arXiv:1809.02921 (2018). [Paper]
Personalized fairness-aware re-ranking for microlending Liu, Weiwen, et al. Proceedings of the 13th ACM conference on recommender systems. 2019. [Paper]
Opportunistic Multi-aspect Fairness through Personalized Re-ranking Sonboli, Nasim, et al. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020. [Paper]
FA*IR: A Fair Top-k Ranking Algorithm Zehlike, Meike, et al. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017. [Paper] [Code]
