We study the implementation of a job recommender system dedicated to the jobseekers at the French Public Employment Service (PES). For each job seeker, this algorithm ranks the available job ads according to the job seeker's chances of being hired on each of them, using a state of the art maching learning algorithm trained on data on past hires recorded by the PES.
The purpose of this experiment is to conduct a large scale beta test of this recommender system. The goal of our intervention is to find an optimal combination between two job vacancy rankings: a ranking based on the chances of a hire taking place and another based on the suitability of the job to the jobseeker's explicit search criteria. The first ranking, called Ranking_ML (ML for machine learning), is obtained from the aforementioned recommender system. The latter is inspired by the matching score currently used at the French PES, which is computed as the weighted sum of the intensities of alignment between the job seeker's search criteria and the job posting's characteristics and requirements (e.g. job seeker's experience vs. required experience, salary sought vs. proposed salary, etc.). We call it Ranking_S (S for matching Score). This intervention will also allow us to investigate the impact of providing jobseekers with information about the ranks of recommended job ads within these two rankings.
This intervention proceeds as follows:
- Jobseekers receive an invitation email
- By clicking on a link in the email, jobseekers access an online survey
- Jobseekers are first exposed to two recommended vacancies. They are asked to evaluate: the overall relevance of the recommendation, their perception of their own chances of recruitment and their assessment of the fit with their search criteria.
- After having rated these two recommended vacancies, the jobseekers access a final page including the 2 previous ads, as well as 8 additional recommendations. They do not have to rate these 8 additional vacancies. On this final page, they have the possibility to click on recommended job ads in order to view further details and apply.