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[2602.22412] A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection
Summary
This article presents a learning-based hybrid decision framework for matching systems, focusing on user departure detection to enhance market efficiency in dynamic environments.
Why It Matters
The proposed framework addresses critical challenges in matching markets, such as kidney exchanges and freight exchanges, by balancing the trade-offs between immediate and delayed matching. This adaptability can lead to improved efficiency and reduced congestion, making it relevant for researchers and practitioners in machine learning and market design.
Key Takeaways
- The framework combines immediate and delayed matching strategies to optimize performance.
- It utilizes user departure data to inform decision-making on matching delays.
- The approach can significantly reduce waiting times and congestion in matching markets.
- Dynamic adjustment allows for flexibility between greedy and patient matching policies.
- The framework is applicable to various fields, including economics and human-computer interaction.
Computer Science > Machine Learning arXiv:2602.22412 (cs) [Submitted on 25 Feb 2026] Title:A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection Authors:Ruiqi Zhou, Donghao Zhu, Houcai Shen View a PDF of the paper titled A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection, by Ruiqi Zhou and 2 other authors View PDF HTML (experimental) Abstract:In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. The benefits of delay are highly sensitive to participants' sojourn times and departure behavior, and delaying matches can impose significant costs, including longer waiting times and increased market congestion. These competing effects make fixed matching policies inherently inflexible in dynamic environments. We propose a learning-based Hybrid framework that adaptively combines immediate and delayed matching. The framework continuously collects data on user departures over time, estimates the underlying departure distribution via regression, and determines whether to delay matching in the subsequent period based on a decision threshold that governs the system's tolerance for matching efficiency loss. The proposed framework can substantially reduce waiting times and congestion while sacrificing only a limited amount of matching efficiency. By dynamically adjusting its matching strategy, the Hybrid framework enables s...
