Personalized school recommendations increase acceptance without overcrowding
Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match
Computers and Society
Summary
When many students receive the same school recommendations, it can lead to overcrowding and reduce their chances of getting in. The authors studied this problem in New York City's high school admissions and found that simple recommendations can worsen outcomes for some students. They designed a smarter system that predicts where congestion might happen and avoids it, helping students get into better nearby schools. Testing their approach in a real admissions cycle showed more students ranked and matched to recommended schools without anyone being rejected from those options.
What this means in practice
- •For school district administrators: Optimize personalized school choice recommendations to improve student match outcomes without causing overcrowding.
- •For online marketplace operators: Design recommendation systems that account for capacity limits to prevent congestion and improve user allocation in competitive markets.
Authors
Erica Chiang, Kenny Peng, Rebecca Lichtenstein, Brielle McDaniel, Kristen O'Neil, Deja Thomas, Lianna Wright, Jon Kleinberg, Eva Tardos, Nikhil Garg
Abstract
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.