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Abstract:
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We describe a two-stage model for identifying the best-suited candidates for a given role that (1) learns hiring manager preferences for the role; (2) can be updated frequently, with low latency; and (3) scales to a very large number of roles. In the first stage, dozens of candidate-role features are modeled in a regression across all applications and roles. The feature weights are subsequently fed as priors into n individual Bayesian models representing n roles. Cross-validated results indicate this approach improves classification accuracy, with the area under the curve of the receiver operating characteristic improving from 71.8% to 77.0%.
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