Extracting Relations Between Sectors

TL;DR

This paper investigates sector similarities using relational analysis and various algorithms to improve job recommendation and candidate matching in the recruitment industry.

Contribution

It introduces a comparative analysis of multiple algorithms for discovering sector similarities from real-world recruitment data.

Findings

Algorithms differ in effectiveness for sector similarity detection.

Relational analysis improves job and candidate matching accuracy.

Insights aid in enhancing recruitment recommendation systems.

Abstract

The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions. The latter holds significant importance when available candidates in a specific sector are also scarce; hence, finding candidates from similar sectors becomes crucial. This work focuses on discovering possible sector similarities through relational analysis. We employ several algorithms from the frequent pattern mining and collaborative filtering domains, namely negFIN, Alternating Least Squares, Bilateral Variational Autoencoder, and Collaborative Filtering based on Pearson's Correlation, Kendall and Spearman's Rank Correlation coefficients. The algorithms are compared on a real-world dataset supplied by a major recruitment company, Kariyer.net, from Turkey. The insights and methods gained through this work are expected to increase the efficiency and accuracy of various methods, such as recommending jobs or finding suitable candidates for open positions.