  This paper provides a review of the job recommender system (JRS) literature
published in the past decade (2011-2021). 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.
