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[2602.15752] Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Summary
This paper introduces a new approach to two-sided matching platforms, focusing on maximizing user retention rather than just match quantity or fairness.
Why It Matters
User retention is critical for platforms like online dating and recruiting, which often struggle with user abandonment. This research provides a novel algorithm, MRet, that prioritizes retention, potentially improving user experience and platform sustainability.
Key Takeaways
- Conventional matching algorithms prioritize match quantity, risking user abandonment.
- MRet algorithm focuses on maximizing user retention through personalized recommendations.
- Empirical evaluations show MRet outperforms traditional methods in retaining users.
Computer Science > Machine Learning arXiv:2602.15752 (cs) [Submitted on 17 Feb 2026] Title:Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching Authors:Ren Kishimoto, Rikiya Takehi, Koichi Tanaka, Masahiro Nomura, Riku Togashi, Yoji Tomita, Yuta Saito View a PDF of the paper titled Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching, by Ren Kishimoto and 6 other authors View PDF HTML (experimental) Abstract:On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates an imbalance, where some users receive far too many matches while many others receive very few and eventually abandon the platform. Retaining users is crucial for many platforms, such as those that depend heavily on subscriptions. Some may use fairness objectives to solve the problem of match maximization. However, fairness in itself is not the ultimate objective for many platforms, as users do not suddenly reward the platform simply because exposure is equalized. In practice, where user retention is often the ultimate goal, casually relying on fairness will leave the optimization of retention up to luck. In this work, instead of maximizing matches or axiomatically defining fairness, we formally define the new problem setting of maximizing user retention in two-sided matching platforms. To this end, we introduce a dynamic learning-to-rank (LT...