A Survey on Bandit Learning in Matching Markets

Li, Shuai; Wang, Zilong; Kong, Fang

doi:10.24963/ijcai.2025/1171

A Survey on Bandit Learning in Matching Markets

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10546-10554.

https://doi.org/10.24963/ijcai.2025/1171

Keywords

Machine Learning: ML: Multi-armed bandits
Machine Learning: ML: Online learning
Machine Learning: ML: Game Theory

Abstract

The two-sided matching market problem has attracted extensive research in both computer science and economics due to its wide-ranging applications in multiple fields. In various online matching platforms, market participants often have unclear preferences. As a result, a growing area of research focuses on the online scenario. Here, one-side participants (players) gradually figure out their unknown preferences through multiple rounds of interactions with the other-side participants (arms). This survey comprehensively reviews and systematically organizes the abundant literature on bandit learning in matching markets. It covers not only existing theoretical achievements but also various other related aspects. Based on the current research, several distinct directions for future study have emerged. We are convinced that delving deeper into these directions could potentially yield theoretical algorithms that are more suitable for real-world situations.

Shuai Li, Zilong Wang, and Fang Kong. 2025. A Survey on Bandit Learning in Matching Markets. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10546–10554. https://doi.org/10.24963/ijcai.2025/1171

@inproceedings{ijcai2025p1171,
  title     = {A Survey on Bandit Learning in Matching Markets},
  author    = {Li, Shuai and Wang, Zilong and Kong, Fang},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10546--10554},
  year      = {2025},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2025/1171},
  url       = {https://doi.org/10.24963/ijcai.2025/1171},
}

TY  - CPAPER
TI  - A Survey on Bandit Learning in Matching Markets
AU  - Li, Shuai
AU  - Wang, Zilong
AU  - Kong, Fang
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10546
EP  - 10554
DO  - 10.24963/ijcai.2025/1171
UR  - https://doi.org/10.24963/ijcai.2025/1171
ER  -