Icims, Inc.

Shiying Chen

Adrian Mihai

An apparatus includes a processor and a memory storing instructions to cause the processor to receive (1) user data associated with a user, (2) target workforce data, (3) target role data, and (4) candidate pool data, randomly select a plurality of candidates from the candidate pool data, execute a machine learning model to generate matching scores for the user and each candidate to the target role data, to produce a matching score distribution, and generate, via a statistical model, a fit score for the user based on a number of candidates in a subset of candidates, where each candidate from the subset of candidates has a matching score lower than the user matching score.

Methods and apparatus for generating a compound presentation that evaluates users and refining job listings using machine learning based on fit scores of users and extracted identifiers from job listings data

In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.

Growing community of inventors

Highland Park, NJ, United States of America

Shiying Chen