User Modeling of Skills and Expertise from Resumes
Hua Li, Daniel J. T. Powell, Mark Clark, Tifani O'Brien, Rafael Alonso · 2015
Job applicants describe their skills and expertise in resumes and curriculum vitaes (CVs). These biographic data are often evaluated by human resource personnel or a search committee. This manual approach works well when the number of resumes is small. However, in this information age, the volume of available resumes can be overwhelming and there is a need for automatic evaluation of applicant skills and expertise. In this paper, we describe a user modeling algorithm to quantitatively identify skills and expertise from biographic data. This algorithm is called REMA (Resume Expertise Modeling Algorithm). REMA takes data from a resume document as input and produces an expertise model. The expertise model details the expertise topics for which the resume owner has claimed competency. Each topic carries a weight indicating the level of competency. There are two key insights for this algorithm. First, one’s expertise is the cumulative result of the various “learning events” in one’s career. These learning events are mentioned in various sections of the resume, such as earning a degree, writing a paper, or getting a patent. Second, one’s knowledge and skills can become outdated or forgotten over time if not reinforced by learning. We have developed a prototype resume evaluation system based on REMA and are in the process of evaluating REMA’s performance.