e-Recruitment recommender systems: a systematic review
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e-Recruitment recommender systems: a systematic review Mauricio Noris Freire1
· Leandro Nunes de Castro1
Received: 28 October 2019 / Revised: 15 October 2020 / Accepted: 18 October 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Recommender Systems (RS) are a subclass of information filtering systems that seek to predict the rating or preference a user would give to an item. e-Recruitment is one of the domains in which RS can contribute due to presenting a list of interesting jobs to a candidate or a list of candidates to a recruiter. This study presents an up-to-date systematic review of recommender systems applied to e-Recruitment considering only papers published from 2012 up to 2020. We searched three databases for published journal articles, conference papers and book chapters. We then evaluated these works in terms of which kinds of RS were applied for e-Recruitment, what kind of information was used in the e-Recruitment RS, and how they were assessed. A total of 896 papers were collected, out of which sixty three research works were included in the survey based on the inclusion and exclusion criteria adopted. We divided the recommender types into five categories (Content-Based Recommendation 26.98%; Collaborative Filtering 6.35%; Knowledge-Based Recommendation 12.7%; Hybrid approaches 20.63%; and Other Types 33.33%); the types of information used were divided into four categories (Social Network 38.1%; Resumés and Job Posts 42.85%; Behavior or Feedback 12.7%; and Others 6.35%), and the assessment types were categorized into four types (Expert Validation 20.83%; Machine Learning Metrics 41.67%; Challenge-specific Metrics 22.92%; and Utility measures 14.58%). Although in many cases a paper may belong to more than one category for each evaluation axis, we chose the most predominant one for our categorization. In addition, there is a clear trend for hybrid and non-traditional techniques to overcome the challenges of e-Recruitment domain. Keywords e-Recruitment · Job recommender systems · Systematic review · Recommendation methods
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Mauricio Noris Freire [email protected] Leandro Nunes de Castro [email protected]
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Natural Computing and Machine Learning Laboratory (LCoN), Mackenzie Presbyterian University UPM, São Paulo, Brazil
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M. N. Freire, L. N. de Castro
1 Introduction E-Recruitment Platforms emerged as a feasible solution to help with the problem of allocating professionals, decreasing the recruitment time and advertisement costs, but, at the same time, increasing the data volume with which the Human Resource Management professionals must deal with. Recommender Systems (RS) have, for long, been applied to help users finding items (e.g., goods or services) that match their personal interests [7]. RSs are information filtering systems that deal with the information overload problem by filtering relevant pieces of information from dynamically generated data and by capturing users’ preferences, interests, or observed behaviors about items. They can predict at what
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