Job-search services like AfterCollege are racing to develop software that thinks and acts like a human recruiter, examining what kinds of workers and companies are drawn to each other so a computer can recommend vacancies to job seekers and candidates to hiring managers. With unemployment at 7.7 percent, more than 12 million Americans are looking for work while almost 4 million openings go unfilled, according to data from the Bureau of Labor Statistics. Targeted matches may help the labor market work better, according to Alvin Roth, an economist at Stanford University near Palo Alto, California.
“There are all kinds of inefficiencies that these firms are trying to solve,” said Roth, who shared last year’s Nobel Prize in economics for his research in matching markets. “It might be hard for me to know about the job that’s available. There might be some skill you’re looking for but you might not find me.”
San Francisco-based AfterCollege and competitors such asCareerBuilder Inc. (CBDR) and Burning Glass are taking advantage of mountains of information they have stored over the years. Using resumes that job-seekers registered, the services extracted data like the degrees and certifications workers had acquired, their geographic locations, past job titles and previous employers and when and how long they held positions. From the job descriptions employers posted, the services also collected and organized information like job title, employer, education and experience requirements of positions as well as the benefits they list.
Engineers then looked for patterns. What kind of worker tends to view, apply to and eventually land what kind of jobs? What kind of employer typically targets what kind of candidate?
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