并非所有匹配都具有同等价值：求职匹配平台中以留存为核心的推荐在线实验
Not All Matches Are Equally Valuable: An Online Experiment of Retention-Focused Recommendation in a Job-Matching Platform
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中文总结 AI 辅助
该研究针对求职匹配平台优化了以留存为核心的推荐方法，通过在线实验验证其可降低用户流失风险，为双边匹配平台的推荐系统设计提供了新方向。
中文摘要 AI 辅助
双边匹配平台中的推荐系统通常针对点击率、回复率或成功匹配总数等即时参与信号进行优化。然而在现实市场中，仅最大化匹配数可能与用户流失率、平台收入等业务目标不一致，尤其是当匹配数较少的用户流失风险显著更高时。本文研究了一个真实求职匹配平台，发现近期匹配数极少的用户确实更可能离开平台，而已成功用户的额外匹配对留存的边际价值有限。基于这一实证发现，我们构建了一个感知留存的推荐问题，并实现了一种简单的后处理方法，用于调整基于匹配的基线排序以防止用户流失。具体而言，该方法为流失风险用户提供评分提升，以增加其获得匹配的可能性并改善留存。我们在真实求职匹配平台的在线实验中评估了这一实用方法。实验组用户流失率呈下降趋势，尽管在常规水平下估计效应未达到统计显著性，而平台侧流失未出现恶化迹象。据我们所知，这是首批在真实互惠求职匹配平台中研究以留存为核心的推荐的在线实验研究之一。
英文摘要
Recommender systems in two-sided matching platforms are commonly optimized for immediate engagement signals such as click-through rate, reply rate, or the total number of successful matches. However, in real-world marketplaces, maximizing matches alone may be misaligned with business goals such as user churn rate and platform revenue, especially when users with fewer matches are at substantially higher risk of churn. In this paper, we study a real job matching platform and show that users with very few recent matches are indeed much more likely to leave the platform, while additional matches for already successful users provide limited marginal value for retention. Motivated by this empirical finding, we formulate a retention-aware recommendation problem and implement a simple post-processing method that adjusts the baseline match-focused ranking to prevent user churn. Specifically, the implemented method gives a score boost to churn-risk users with the goal of increasing their likelihood of obtaining matches and improving retention. We evaluate this practical approach in an online experiment on a real job-matching platform. The treatment group showed directionally lower user churn than the control group, although the estimated effect was not statistically significant at conventional levels, while company-side churn showed no evidence of deterioration. To our knowledge, this is among the first online experimental studies to investigate retention-focused recommendation in a real reciprocal job-matching platform.
