Conversational Recommendations for Job Recruiters (Long paper)

Recruitment is a long, complex, and crucial process for every company. Matching candidates to job offers is time-consuming and requires a broad knowledge of the different domains, in particular the relevant skills and qualifications and their relationships. By comparing the required skills with candidates’ profiles, recruiters can identify potential collaborators for a given position. To support recruiters’ work, we propose recommendation techniques that help the identification of suitable candidates. While job offers contain required skills for a position, the recruiter’s preferences are not explicit: for example, some skills might be more important than others etc. These preferences are very hard to elicit, and they even might depend on the particular job offer. We propose conversational recommendation techniques that can support recruiters’ work and recommend candidates to a given job offer, based on relevant skills, that can provide an explanation for the recommendation, so that the recruiter has specific information as to why a candidate is recommended. Interaction with the system that can reveal more details about preferences is possible. In this way, a new set of recommendations can be obtaines or he can reinitialize the recommendation procedure, if preferred. Our system is evaluated using a real Resume/Job Offer dataset from a company database. Performance of generated recommendations is compared to a reference Deep-Learning based matching system trained on the same dataset.