MatchXplain: Analyzing Preferences, Explaining Outcomes, and Simplifying Decisions
Abstract
Matching markets, where agents are assigned to one another based on preferences and constraints, are fundamental in various AI-driven applications such as school choice, content matching, and recommender systems. A key challenge in these markets is understanding preference data, as the interpretability of algorithmic solutions hinges on accurately capturing and explaining preferences. We introduce MatchXplain, a platform that integrates preference explanation with a robust matching engine. MatchXplain offers a layered approach for explaining preferences, computing diverse matching solutions, and providing interactive visualizations to enhance user understanding. By bridging algorithmic decision-making with explainability, MatchXplain improves transparency and trust in algorithmic matching markets.
Cite
Text
Hosseini et al. "MatchXplain: Analyzing Preferences, Explaining Outcomes, and Simplifying Decisions." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1261Markdown
[Hosseini et al. "MatchXplain: Analyzing Preferences, Explaining Outcomes, and Simplifying Decisions." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/hosseini2025ijcai-matchxplain/) doi:10.24963/IJCAI.2025/1261BibTeX
@inproceedings{hosseini2025ijcai-matchxplain,
title = {{MatchXplain: Analyzing Preferences, Explaining Outcomes, and Simplifying Decisions}},
author = {Hosseini, Hadi and Jing, Yubo and Singh, Ronak},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {11053-11057},
doi = {10.24963/IJCAI.2025/1261},
url = {https://mlanthology.org/ijcai/2025/hosseini2025ijcai-matchxplain/}
}