In:
Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35, No. 18 ( 2021-05-18), p. 15885-15886
Abstract:
We propose a recommender system that, starting from a set of users skills, identifies the most suitable jobs as they emerge from a large text of Online Job Vacancies (OJVs). To this aim, we process 2.5M+ OJVs posted in three different countries (United Kingdom, France and Germany), generating several embeddings and performing an intrinsic evaluation of their quality. Besides, we compute a measure of skill importance for each occupation in each country, the Revealed Comparative Advantage (rca). The best vector models, together with the rca, are used to feed a graph database, which will serve as the keystone for the recommender system.
Finally, a user study of 10 validates the effectiveness of Skills2Job, both in terms of precision and nDGC.
Type of Medium:
Online Resource
ISSN:
2374-3468
,
2159-5399
DOI:
10.1609/aaai.v35i18.17939
Language:
Unknown
Publisher:
Association for the Advancement of Artificial Intelligence (AAAI)
Publication Date:
2021
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