Job Recommender Systems: A Review

TL;DR

This review analyzes a decade of job recommender systems literature, emphasizing temporal, reciprocal, and fairness considerations, and categorizing hybrid models to understand their design and validation challenges.

Contribution

It provides a detailed taxonomy of hybrid JRS models, highlights overlooked fairness issues, and discusses the impact of data availability on model choice and validation.

Findings

Temporal and reciprocal factors improve model performance.

Fairness discussions are rare and often oversimplified.

Model generalizability across datasets is limited.

Abstract

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.