The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets

Abstract

Choosing job applicants to hire in online labor markets is hard. To identify the best applicant at hand, employers need to assess a heterogeneous population. Recommender systems can provide targeted job-applicant recommendations that help employers make better-informed and faster hiring choices. However, existing recommenders that rely on multiple user evaluations per recommended item (e.g., collaborative filtering) experience structural limitations in recommending job applicants: Because each job application receives only a single evaluation, these recommenders can only estimate noisy user-user and item-item similarities. On the other hand, existing recommenders that rely on classification techniques overcome this limitation. Yet, these systems ignore the hired worker’s performance—and, as a result, they uniformly reinforce prior observed behavior that includes unsuccessful hiring choices—while they overlook potential sequential dependencies between consecutive choices of the same employer. This work addresses these shortcomings by building a framework that uses job-application characteristics to provide recommendations that (1) are unlikely to yield adverse outcomes (performance-aware) and (2) capture the potentially evolving hiring preferences of employers (sequence-aware). Application of this framework on hiring decisions from an online labor market shows that it recommends job applicants who are likely to get hired and perform well. A comparison with advanced alternative recommender systems illustrates the benefits of modeling performance-aware and sequence-aware recommendations. An empirical adaptation of our approach in an alternative context (restaurant recommendations) illustrates its generalizability and highlights its potential implications for users, employers, workers, and markets. This paper was accepted by Kartik Hosanagar, information systems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4690 .