Making the Cut: A Bandit-based Approach to Tiered Interviewing

Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Usin...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Schumann, Candice, Lang, Zhi, Foster, Jeffrey S, Dickerson, John P
Format: Article
Language:English
Subjects:
Algorithms
Best practice
Combinatorial analysis
Human resources
Interviews
Software
Online Access:Get full text
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Summary:Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Using accepted hiring models and the concept of structured interviews, a best practice in human resources, we cast tiered hiring as a combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting. The goal is to select a subset of arms (in our case, applicants) with some combinatorial structure. We present new algorithms in both the probably approximately correct (PAC) and fixed-budget settings that select a near-optimal cohort with provable guarantees. We show via simulations on real data from one of the largest US-based computer science graduate programs that our algorithms make better hiring decisions or use less budget than the status quo.
ISSN:2331-8422