A Deep Choice Model for Hiring Outcome Prediction in Online Labor Markets

Abstract

A key challenge faced by online labor market researchers and practitioners is to understand how employers make hiring decisions from many job bidders with distinct attributes. This study investigates employer hiring behavior in one of the largest online labor markets by building a datadriven hiring decision prediction model. With the limitation of traditional discrete choice model (conditional logit model), we develop a novel deep choice model to simulate the hiring behavior from 722,339 job posts. The deep choice model extends the classical conditional logit model by learning a non-linear utility function identically for each bidder within of the job posts via a pointwise convolutional neural network. This non-linear mapping can be straightforwardly optimized using stochastic gradient approach. We test the model on 12 categories of job posts in the dataset. Results show that our deep choice model outperforms the linear-utility conditional logit model in predicting hiring preferences. By analyzing the model using dimensionality reduction and sensitivity analysis, we highlight the nonlinear combination of bidders’ features in impacting employers’ hiring decisions.