An algorithm that uses job-specific keywords can help applicants improve their position by at least 16 spots on average in a pool of 100 applicants, according to Shirin Nilizadeh, assistant professor of computer science at the University of Texas at Arlington (UTA).
"We found out that you can tailor your resume for a specific job by using specific keywords that could get you pushed toward the top," says Nilizadeh, an author of "Attacks Against Ranking Algorithms with Text Embeddings: A Case Study on Recruitment Algorithms," published in the Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP.
Text-embedding algorithms used in job recruiting pair words and sentences in resumes with a job description to produce similarity scores on which resumes are ranked. The authors' work found that while adding more keywords improves the ranking, adding too many might not have the same effect.
Hong Jiang, chair in the department of computer science at UTA, says the work "might be a tool prospective employees and employers could use in the job search process."
From University of Texas at Arlington
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