@misc{01GMB1GTST7671VQGX3SGCZTRB,
  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.
}},
  author       = {{Mashayekhi, Yoosof and Li, Nan and Kang, Bo and Lijffijt, Jefrey and De Bie, Tijl}},
  language     = {{eng}},
  pages        = {{28}},
  title        = {{A challenge-based survey of e-recruitment recommendation systems}},
  year         = {{2022}},
}