Communications of the ACM
Paper Describes Baidu Neural Net Approach to Match Job Openings With Candidates
A team at the Chinese technology firm Baidu has presented a proposed neural network trained to analyze resumes to determine which job candidates best match job posting descriptions.
In a paper published in ACM Transactions on Management Information Systems, the team says that the Person-Job Fit Neural Network (PJFNN) "can effectively learn the joint representation of Person-Job fitness from historical job applications." The system can then flag relevant job seekers.
The team ran experiments on job application records from a high-tech Chinese company featuring more than 2 million resumes and 15,039 job postings, of which there were only 31,928 successful applications. "Although PJFNN cannot learn good representations for all of the requirements, the latent vectors of most resumes and job postings learned by PJFNN are meaningful generally and can help to improve the effectiveness and efficiency of Person-Job Fit," the researchers write.
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