---
title: "MatchXplain: Analyzing Preferences, Explaining Outcomes, and Simplifying Decisions"
authors:
  - "Hadi Hosseini"
  - "Yubo Jing"
  - "Ronak Singh"
doi: "10.24963/ijcai.2025/1261"
url: "https://ijcai.org/proceedings/2025/1261"
pdf: "https://ijcai.org/proceedings/2025/1261.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11053-11057"
track: "Demo Track"
keywords:
  - "Agent-based and Multi-agent Systems: MAS: Engineering methods, platforms, languages and tools"
  - "Game Theory and Economic Paradigms: GTEP: Computational social choice"
  - "Humans and AI: HAI: Human-AI collaboration"
  - "Game Theory and Economic Paradigms: GTEP: Auctions and market-based systems"
---

# MatchXplain: Analyzing Preferences, Explaining Outcomes, and Simplifying Decisions

Hadi Hosseini, Yubo Jing, Ronak Singh

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11053-11057. DOI: [10.24963/ijcai.2025/1261](https://doi.org/10.24963/ijcai.2025/1261)

## 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.