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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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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13356)

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].

Keywords

  • AI in education
  • Recommender system
  • Recommendation system
  • Up-skilling
  • Natural language processing

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Fig. 1.

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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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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