Explainable Job Recommender Systems for Recruiters and Consulting Companies

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Recruitment has always been a crucial task for the success of companies, and especially consulting companies for which hiring is a centerpiece of their business model. The growth of the labor market along the increasing number of specialized skills that are required by companies has motivated the exploration of techniques to optimize and even automate parts of the recruitment process.

The numerous progress made in the fields of Artificial Intelligence and Natural Language Processing during the past few decades offered the opportunity to efficiently process the data used during the recruitment.

We examine the use of a job recommender system in a consulting company, with a focus on the explanation of the recommendation and its perception by users. First, we experiment with knowledge-based recommendations using the European ontology of skills and occupation ESCO which showcases promising results, but because of current limitations, we finally use a semantic-based recommender system that has since become part of the company processes and offers the opportunity for qualitative and quantitative studies on the impact of the recommendations and its explanations.

We link the explanation availability to major gains in efficiency for recruiters. It also offers them a valuable way to fine-tune recommendations through contextual feedback. Such a feedback is not only useful for generating recommendations at run-time, but also for providing valuable data to evaluate models and further improve the system. Going forward we advocate that the availability of recommendations should be the standard for every job recommender systems.