e-Recruitment recommender systems: a systematic review

摘要

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.