发表机构
Cornell University; New York City Public Schools（康奈尔大学; 纽约市公立学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对推荐引发拥堵问题，提出拥堵感知的双层优化-模拟方法，在纽约市高中匹配中部署，随机试验显示推荐显著提升匹配率且无拒绝，表明推荐应作为市场塑造干预设计。
AI 中文摘要
算法推荐可以帮助参与者在大型匹配市场中导航。例如，学校和大学选择的推荐可以减少信息摩擦以及获得高绩效项目机会的差异。然而，在大规模应用中，容量受限环境下的推荐系统可能适得其反：如果推荐系统将过多用户导向同一项目，那么即使最初预测匹配该项目概率较高的用户，也可能因竞争加剧而无法匹配成功。本文形式化了这种推荐引发的拥堵现象；受纽约市高中匹配的启发，我们表明，天真的推荐可能导致项目录取率急剧下降，最受影响的是附近选择最少的申请者。接下来，我们提出并理论分析了一种拥堵感知的双层优化-模拟方法，用于分配推荐，并在均衡状态下安全地改善匹配结果。最后，我们在2025-26招生周期的纽约市高中匹配中部署了该方法，旨在通过突出个性化的附近高绩效项目列表（申请者对这些项目有较高的预测录取可能性）来减少差异。在一项随机对照试验中，我们发现16.4%的受试组申请者将推荐项目列入排名，而对照组中有10.5%的申请者将本会被推荐的项目列入排名（相对增加57%；p=0.011）；5.6%的受试组申请者匹配到此类项目，而对照组为3.3%（相对增加71%；p=0.071）；此外，没有受试组申请者被推荐项目拒绝。我们的研究结果表明，推荐系统应被视为塑造市场的干预措施来分析和设计。
英文摘要
Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and disparities in access to high-performing programs. At scale, however, recommenders in capacity-constrained settings can be self-defeating: if they steer too many users toward the same items, then even users who were originally predicted to have a high chance of matching to an item may not, due to increased competition. In this paper, we formalize this phenomenon of recommendation-induced congestion; motivated by the NYC high school match, we show that naive recommendations can cause sharp decreases in program acceptance rates, most affecting applicants with the fewest nearby options. Next, we propose and theoretically analyze a congestion-aware, bilevel optimize-and-simulate approach to allocate recommendations and improve match outcomes safely, in equilibrium. Finally, we deploy this approach in the 2025-26 admissions cycle of the NYC high school match, aiming to reduce disparities by highlighting personalized lists of nearby, high-performing programs where an applicant has a high predicted offer likelihood. In a randomized controlled trial, we find that 16.4% of treatment applicants ranked a recommended program, versus 10.5% of control applicants who ranked a program they would have been recommended (57% relative increase; $p$=0.011); 5.6% of treatment applicants matched to such a program, versus 3.3% of control applicants (71% relative increase; $p$=0.071); further, no treatment applicant was rejected from a recommended program. Our findings suggest that recommenders should be analyzed and designed as market-shaping interventions.
CommentsPreliminary version in ACM EC 2026
