E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic 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. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, 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 most promising in the e-recruitment recommendation domain.

电子招聘推荐系统向求职者推荐职位，求职者向招聘人员推荐职位。这些推荐是根据求职者对职位的适合性以及求职者和招聘人员的偏好而生成的。因此，电子招聘推荐系统可能会极大地影响人们的职业生涯。此外，通过影响公司的招聘流程，电子招聘推荐系统在塑造公司的竞争优势方面发挥着重要作用。因此，考虑电子招聘中的推荐系统面临哪些（独特的）挑战似乎是明智的。关于这个主题的现有调查从算法的角度讨论了过​​去的研究，例如，将它们分类为协同过滤、基于内容和混合方法。相反，这项调查采用了一种补充性的、基于挑战的方法。我们相信，这对于面临具体的电子招聘设计任务和一系列特定挑战的开发人员以及在该领域寻找有影响力的研究项目的研究人员来说更加实用。在本次调查中，我们首先确定了电子招聘推荐研究中的主要挑战。接下来，我们讨论文献中如何研究这些挑战。最后，我们提供了我们认为在电子招聘推荐领域最有前途的未来研究方向。