JoLA: Job Landscape Aware Job Recommendation

Job recommendation (JR), among the most critical challenges of AI, aims to alleviate frictional unemployment with major potential impacts on society and economy at large. However, Job Recommender Systems (JRS) might become counter-productive and create a congestion phenomenon, if job seekers are mostly recommended the most popular job ads. This paper proposes a novel perspective on JRS, observing that the job market tends to involve a number of so-called “orphan" job ads, that receive very few or no applications. The orphan-job phenomenon is detrimental to the job market as it mechanically decreases the number of jobs effectively considered, worsening the market imbalance and increasing the congestion; in the long term, it also tends to prevent companies from publishing other ads, de facto creating a sleeping job market that is not revealed to the job seekers. This paper introduces new JRS losses, aimed to prevent both the congestion and the orphan-jobs phenomenon, based on a differentiable approximation of the market share attributed to a job ad. The resulting so-called Job Landscape Aware recommender system (JoLA) is experimentally assessed and compared with the state of the art on public datasets, showing new trade-offs that exist between standard recommendation metrics and congestion, while enforcing the desired exposure for most ads. The JoLA code is publicly available at https://codeberg.org/solal/jola.