The 2021 edition of the IJCAI – the 30th International Joint Conference on Artificial Intelligence – is currently being held from 19 to 26 August.

It is certainly one of the most important conferences in this field.

Within the conference there is a Demonstration Track, at which Lorenzo Malandri (Crisp) presented the following demo:

“Skills2Graph: Processing Million Job Ads to face the Job Skill Mismatch Problem”.

In this paper, we present skills2graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics – computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) – and distributional semantics – generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by skills2graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by skills2graph.

The authors of the article are: Giabelli, Malandri, Mercorio, Mezzanzanica, Seveso.

The article is attached, the demo can be found here: https://youtu.be/Fiz9z_4FSbA