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

arXiv: https://arxiv.org/abs/1707.09751 · HTML: https://mlsanity.com/paper/1707.09751/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness](https://mlsanity.com/paper/2410.05006/) (2024)
- [Turtle Score -- Similarity Based Developer Analyzer](https://mlsanity.com/paper/2205.04876/) (2022)

## Abstract (arXiv)

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.