Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job Description

TL;DR

Skill2vec is a neural network-based method inspired by Word2vec that maps skills into a vector space, improving recruitment search strategies by capturing skill relationships, as validated by domain experts.

Contribution

This paper introduces Skill2vec, a novel neural network approach for representing skills in a vector space to enhance candidate search in recruitment.

Findings

Skill2vec effectively captures skill relationships.

Manual evaluation shows improved recruitment search.

Neural embedding of skills aids in candidate matching.

Abstract

Unsupervise learned word embeddings have seen tremendous success in numerous Natural Language Processing (NLP) tasks in recent years. The main contribution of this paper is to develop a technique called Skill2vec, which applies machine learning techniques in recruitment to enhance the search strategy to find candidates possessing the appropriate skills. Skill2vec is a neural network architecture inspired by Word2vec, developed by Mikolov et al. in 2013. It transforms skills to new vector space, which has the characteristics of calculation and presents skills relationships. We conducted an experiment evaluation manually by a recruitment company's domain experts to demonstrate the effectiveness of our approach.