Using Machine Learning with Eye-Tracking Data to Predict if a Recruiter Will Approve a Resume

Angel Pina

Corbin Petersheim

Josh Cherian

Joanna Nicole Lahey

Gerianne Alexander

Tracy Hammond

When job seekers are unsuccessful in getting a position, they often do not get feedback to inform them on how to develop a better application in the future. Therefore, there is a critical need to understand what qualifications recruiters value in order to help applicants. To address this need, we utilized eye-trackers to measure and record visual data of recruiters screening resumes to gain insight into which Areas of Interest (AOIs) influenced recruiters’ decisions the most. Using just this eye-tracking data, we trained a machine learning classifier to predict whether or not a recruiter would move a resume on to the next level of the hiring process with an AUC of 0.767. We found that features associated with recruiters looking outside the content of a resume were most predictive of their decision as well as total time viewing the resume and time spent on the Experience and Education sections. We hypothesize that this behavior is indicative of the recruiter reflecting on the content of the resume. These initial results show that applicants should focus on designing clear and concise resumes that are easy for recruiters to absorb and think about, with additional attention given to the Experience and Education sections.

This publication uses Eye Tracking and Eye Tracking Screen Based which is fully integrated into iMotions Lab

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