A challenge-based survey of e-recruitment recommendation systems

TL;DR

This paper presents a challenge-based survey of e-recruitment recommendation systems, focusing on practical research challenges and future directions rather than traditional algorithmic categorization.

Contribution

It introduces a challenge-oriented framework for analyzing e-recruitment recommendation systems, offering practical insights for developers and researchers.

Findings

Identified key challenges in e-recruitment recommendation research.

Reviewed how existing studies address these challenges.

Suggested promising future research directions.

Abstract

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences. Therefore, e-recruitment recommendation systems could greatly impact job seekers' careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the companies' competitive edge in the market. Hence, the domain of e-recruitment recommendation deserves specific attention. Existing surveys on this topic tend to discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach, which we believe might be more practical to developers facing a concrete e-recruitment design task with a specific set of challenges, as well as to researchers looking for impactful research projects in this domain. We first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider promising in the e-recruitment recommendation domain.