{"type":"ScholarlyArticle","title":"Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem","authors":[{"name":"Anna Giabelli","slug":"anna-giabelli","given":"Anna","family":"Giabelli","affiliation":""},{"name":"Lorenzo Malandri","slug":"lorenzo-malandri","given":"Lorenzo","family":"Malandri","affiliation":""},{"name":"Fabio Mercorio","slug":"fabio-mercorio","given":"Fabio","family":"Mercorio","affiliation":""},{"name":"Mario Mezzanzanica","slug":"mario-mezzanzanica","given":"Mario","family":"Mezzanzanica","affiliation":""},{"name":"Andrea Seveso","slug":"andrea-seveso","given":"Andrea","family":"Seveso","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/708","url":"https://ijcai.org/proceedings/2021/708","pdf":"https://ijcai.org/proceedings/2021/0708.pdf","markdown":"https://ijcai.org/proceedings/2021/708.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/708","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Demo Track","track":"Demo Track","number":708,"pages":{"first":4984,"last":4987},"keywords":["Natural Language Processing: General","Machine Learning: General","Recommender Systems: General","Human-Computer Interaction: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}