Job Recommendation from Semantic Similarity of LinkedIn Users’ Skills

Giacomo Domeniconi, Gianluca Moro, Andrea Pagliarani, Karin Pasini, Roberto Pasolini

Abstract

Until recently job seeking has been a tricky, tedious and time consuming process, because people looking for a new position had to collect information from many different sources. Job recommendation systems have been proposed in order to automate and simplify this task, also increasing its effectiveness. However, current approaches rely on scarce manually collected data that often do not completely reveal people skills. Our work aims to find out relationships between jobs and people skills making use of data from LinkedIn users’ public profiles. Semantic associations arise by applying Latent Semantic Analysis (LSA). We use the mined semantics to obtain a hierarchical clustering of job positions and to build a job recommendation system. The outcome proves the effectiveness of our method in recommending job positions. Anyway, we argue that our approach is definitely general, because the extracted semantics could be worthy not only for job recommendation systems but also for recruiting systems. Furthermore, we point out that both the hierarchical clustering and the recommendation system do not require parameters to be tuned.

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Paper Citation


in Harvard Style

Domeniconi G., Moro G., Pagliarani A., Pasini K. and Pasolini R. (2016). Job Recommendation from Semantic Similarity of LinkedIn Users’ Skills . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 270-277. DOI: 10.5220/0005702302700277


in Bibtex Style

@conference{icpram16,
author={Giacomo Domeniconi and Gianluca Moro and Andrea Pagliarani and Karin Pasini and Roberto Pasolini},
title={Job Recommendation from Semantic Similarity of LinkedIn Users’ Skills},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={270-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005702302700277},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Job Recommendation from Semantic Similarity of LinkedIn Users’ Skills
SN - 978-989-758-173-1
AU - Domeniconi G.
AU - Moro G.
AU - Pagliarani A.
AU - Pasini K.
AU - Pasolini R.
PY - 2016
SP - 270
EP - 277
DO - 10.5220/0005702302700277