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.

翻译：电子招聘推荐系统向求职者推荐职位，同时也向招聘方推荐求职者。推荐结果基于求职者与岗位的匹配程度，以及双方偏好生成。因此，电子招聘推荐系统能够深刻影响求职者的职业生涯，并通过影响企业的招聘流程，在塑造企业市场竞争力方面发挥重要作用。该领域值得特别关注。现有相关综述倾向于从算法角度探讨已有研究（例如将其分为协同过滤、基于内容和混合方法）。本综述则采用互补的、基于挑战的方法，我们认为这种方法对面临具体电子招聘设计任务（需应对特定挑战）的开发者，以及寻找该领域有影响力研究课题的研究人员更为实用。我们首先梳理电子招聘推荐研究中的主要挑战，继而探讨现有文献如何应对这些挑战，最后指出该领域具有前景的未来研究方向。