超越匹配最大化和公平性：以用户留存优化的双侧匹配
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
AI总结：
本文提出了一种新的双侧匹配优化方法，旨在最大化用户留存而非单纯匹配数量或公平性，通过引入动态学习排序算法MRet，利用用户个性化留存曲线优化推荐策略，提升整体用户留存率。
摘要：
在在线约会和招聘等双侧匹配平台上，推荐算法通常旨在最大化总匹配数。然而，这一目标导致了不平衡，一些用户获得过多匹配而另一些用户则获得极少并最终离开平台。对于许多平台，尤其是依赖订阅的平台，用户留存至关重要。一些平台可能使用公平性目标来解决匹配最大化的问题。然而，公平性本身并非所有平台的最终目标，因为用户不会仅仅因为曝光均等而奖励平台。在实践中，用户留存通常是最终目标，随意依赖公平性会使留存优化取决于运气。在本工作中，我们没有最大化匹配或公理化定义公平性，而是正式定义了双侧匹配平台中最大化用户留存的新问题设置。为此，我们引入了一种动态学习到排序（LTR）算法，称为Matching for Retention（MRet）。与传统的双侧匹配算法不同，我们的方法通过从每个用户档案和交互历史中学习个性化留存曲线来建模用户留存。基于这些曲线，MRet通过同时考虑接收推荐的用户和被推荐用户的留存收益，动态调整推荐策略，使得有限的匹配机会分配到最能提高整体留存的地方。自然但重要的是，对主要在线约会平台的合成和真实世界数据集的实证评估显示，MRet实现了更高的用户留存率，因为传统方法优化匹配或公平性而非留存。
英文摘要：
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 (LTR) algorithm called Matching for Retention (MRet). Unlike conventional algorithms for two-sided matching, our approach models user retention by learning personalized retention curves from each user's profile and interaction history. Based on these curves, MRet dynamically adapts recommendations by jointly considering the retention gains of both the user receiving recommendations and those who are being recommended, so that limited matching opportunities can be allocated where they most improve overall retention. Naturally but importantly, empirical evaluations on synthetic and real-world datasets from a major online dating platform show that MRet achieves higher user retention, since conventional methods optimize matches or fairness rather than retention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
