Job Recommender Systems: A Review

Abstract

This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as 'hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.

Integration and Recommendation System of Profiles based on Professional Social Networks

“…The search proposal for the number of recommendations is displayed between two updates by varying K between 5 and 10 (interval taken in the literature) for all the proposals which respectively included ( [1,5], [1,6], [1,7], [1,8], [1,9] and [1,10]), then by recovering each time the return values of the various metrics.…”

“…For Content-based JRS, we use a semantic similarity measure between the user's profile and the set of vacancies, by estimating their respective relevance for the applicant [8]. For example, of [9] proposed, as a method, the segmentation of CV (Curriculum Vitae) into sections ordered by content, for the extraction of terms representing the skills.…”

Flexible multi-view semi-supervised learning with unified graph

“…The job recommender system typically mirrors the methodologies of general recommender systems [4]. Content-based [15,22] and collaborative filtering [19,25] approaches are popular in academic research.…”

“…Content-based [15,22] and collaborative filtering [19,25] approaches are popular in academic research. Knowledge-based systems [13], which focus on matching and similarity in an ontology space [4,21,26], are unique to job recommendation. Recently, hybrid systems and deep neural networks have gained traction [23].…”

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

“…Among the most prevalent types of JRS are Content-Based JRS (CB-JRS), Collaborative Filtering JRS (CF JRS), Hybrid JRS (H-JRS), and Knowledge-Based JRS (KB JRS) [4,5]. Each type has distinct characteristics and methodologies for recommending jobs to users:…”