Finding the Best Job Applicants for a Job Posting: A Comparison of Human Resources Search Strategies

Finding the best candidates to match a set of job requirements can be viewed as both an art and a science. In this paper, we conduct an empirical study using actual job candidates and job applicants. We compare the ranked lists generated by executive recruiting experts with the list generated by three search strategies: one using crowdworkers in a gamified environment, a second using information retrieval-based search methods, and a third method which combines information retrieval methods and weighted feature-based approach. We examine these three strategies across two separate job categories - technical and non-technical (management). Our study finds the gamified-enhanced crowdsourcing environment works best for ranking candidates for technical jobs while the text mining and gamified crowdsourcing environments perform equally well for ranking candidates for non-technical jobs. Last, we discuss possible reasons for our results as well as suggest possible enhancements to reduce the gap between our strategies and the HR executive recruiting experts.