[1G3-OS-13a] OS-13

2:55 PM - 3:10 PM JST(5:55 AM - 6:10 AM UTC)

[1G3-OS-13a-06] Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching

〇Ren Kishimoto¹, Rikiya Takehi², Koichi Tanaka³, Masahiro Nomura¹, Yoji Tomita⁴, yuta saito⁵, togashi riku⁴ (1. Institute of Science Tokyo, 2. Waseda University, 3. Keio University, 4. CyberAgent, 5. Cornell University)

Keywords:

matching,user retention,learning-to-rank

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 too many matches while others receive very few and eventually abandon the platform. Although fairness objectives are sometimes used to mitigate this issue, fairness itself is often not the ultimate goal; in practice, user retention is more critical. In this work, we formalize the new problem setting of maximizing user retention in two-sided matching platforms. We propose a dynamic learning-to-rank (LTR) algorithm called Matching for Retention (MRet). Unlike conventional approaches, MRet models user retention by learning personalized retention curves from user profiles. Using these curves, MRet dynamically adapts recommendations by jointly considering the retention gains of both the recommending and recommended users. Experiments on synthetic datasets demonstrate that MRet achieves higher user retention.