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Investigating Natural Language Processing Techniques for a Recommendation System to Support Employers, Job Seekers and Educational Institutions

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

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Abstract

Skills are the common ground between employers, job seekers and educational institutions which can be analyzed with the help of natural language processing (NLP) techniques. In this paper we explore a state-of-the-art pipeline that extracts, vectorizes, clusters, and compares skills to provide recommendations for all three parties—thereby bridging the gap between employers, job seekers and educational institutions. Our best system combines Sentence-BERT [1], UMAP [2], DBSCAN [3], and K-means clustering [4].

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Notes

  1. 1.

    “Job seeker” refers to individuals who wish to apply for or advance in a job.

  2. 2.

    https://github.com/KoenBothmer/SkillScanner.

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Authors and Affiliations

  1. IU International University of Applied Sciences, Bad Honnef, Germany

    Koen Bothmer & Tim Schlippe

Authors
  1. Koen Bothmer
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  2. Tim Schlippe
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Corresponding author

Correspondence to Tim Schlippe .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Bothmer, K., Schlippe, T. (2022). Investigating Natural Language Processing Techniques for a Recommendation System to Support Employers, Job Seekers and Educational Institutions. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_90

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_90

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

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