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

Bothmer, Koen; Schlippe, Tim

doi:10.1007/978-3-031-11647-6_90

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…While in [20], we only briefly explained our NLP pipeline for extracting, vectorizing, clustering, and comparing skills, in this paper, we will further elaborate on the technical details. Our goal was to help employers, job seekers, and educational institutions adapt to the market skills.…”

Section: Nlp To Extract Vectorize Cluster and Compare Skillsmentioning

confidence: 99%

Skill Scanner

Schlippe

Bothmer

2023

Int. J. Adv. Corp. Learn.

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Skills are the common ground between employers, job seekers and educational institutions which can be analyzed with the help of artificial intelligence (AI), specifically 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 players—thereby bridging the gap between employers, job seekers and educational institutions. As companies hiring data scientists report that it is increasingly difficult to find a so-called "unicorn data scientist" [1], we conduct our experiments and analysis using companies’ job postings for a data scientist position, job seekers’ CVs for that position, and a curriculum from a master's program in data science. However, our investigated methods and our final recommendation system can be applied to other job positions as well. Our best system combines Sentence-BERT [2], UMAP [3], DBSCAN [4], and K-means clustering [5]. To also evaluate feedback from potential users, we conducted a survey, in which the majority of employers’, job seekers’ and educational institutions’ representatives state that with the help of our automatic recommendations, processes related to skills are more effective, faster, fairer, more explainable, more autonomous and more supported.

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Section: Nlp To Extract Vectorize Cluster and Compare Skillsmentioning

confidence: 99%

Skill Scanner

Schlippe

Bothmer

2023

Int. J. Adv. Corp. Learn.

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“…A Bi-directional recommendation system was developed using NLP techniques to support educational institutions, employers, and job seekers. The system combines models such as BERT, DBSCAN, UMAP, and K-mean clustering to guide society's job and learning recommendations [10]. A study conducted in 2019 focused on evaluating the effectiveness of NLP-based search engines.…”

Section: Literature Reviewmentioning

confidence: 99%

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

subramanian,

Ammunuel

et al. 2023

Preprint

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In the rapidly evolving field of education, the need for a semantic search engine to efficiently retrieve graph-based data is crucial. Universities and colleges generate vast amounts of educational content research articles, and having a semantic search engine can enhance the accuracy of search results, ensuring that students and staff can access the right information effectively. It's imperative to develop an AI-based smart recommendation system equipped with semantic search capabilities to optimize information retrieval and provide accurate mapping for students or scholars. Such a system can revolutionize how educational content is accessed, offering personalized recommendations that align with the individual learning objectives of students and scholars, thus improving their educational experience. KMSBOT is an innovative academic recommendation system designed to enhance the efficiency of existing models. It effectively summarizes academic data and provides tailored information for students, research scholars, and educational faculty. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses the limitations of traditional recommendation systems and contributes to improving user satisfaction and engagement in academic environments.

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Skill Scanner: Connecting and Supporting Employers, Job Seekers and Educational Institutions with an AI-Based Recommendation System

Bothmer

Schlippe

2022

Lecture Notes in Networks and Systems

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

Cited by 8 publications

References 14 publications

Skill Scanner

Skill Scanner

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

Skill Scanner: Connecting and Supporting Employers, Job Seekers and Educational Institutions with an AI-Based Recommendation System

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