Dynamic Recommendations for Sequential Hiring Decisions in Online Labor Markets
Marios Kokkodis (Carroll School of Management, Boston College)
Online labor markets facilitate transactions between employers and a diverse set of independent contractors around the globe. When making hiring decisions in these markets, employers have to assess a large and heterogeneous population of contractors. Because many of the contractors’ characteristics are latent, employers often make risky decisions that end up in negative outcomes. In this work, we address this issue by proposing a framework for recommending contractors who are likely to get hired and successfully complete the task at hand. We start our analysis by acknowledging that employers’ hiring behavior dynamically evolves with time; Employers learn to choose contractors according to the outcomes of their previously completed tasks. To capture this dynamic evolution, we propose a structured Hidden Markov Model that explicitly models task outcomes through the employers’ evolution. We build and evaluate the proposed framework on a dataset of real online hiring decisions. We then compare our approach with a set of previously proposed static algorithms and we show that our proposed framework provides up to 24% improved recommendations. We conclude by discussing the positive impact that such better recommendations of candidates can have on employers, contractors, and the market itself.