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2021 AAAI AAAI 2021

Skills2Job: A Recommender System that Encodes Job Offer Embeddings on Graph Databases (Student Abstract)

Abstract

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.

🌉 Interdisciplinary Bridge — Computer Science and Data Science & Analytics and Deep Learning and Machine Learning
🧭 Keyword Pioneer — job embedding
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Andrea Seveso , Anna Giabelli , Lorenzo Malandri , Fabio Mercorio , Mario Mezzanzanica

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Core Methods > Embedding Learning Data Science & Analytics > Applications > Recommender Systems Computer Science > Applications > Information Retrieval Machine Learning > Application Areas > Recommender Systems Deep Learning > Learning Types > Representation Learning

Keywords

recommender system word embedding graph database job recommendation job embedding online job vacancy revealed comparative advantage vector model
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