Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job Description
This addresses the need for better candidate matching in recruitment, though it is incremental as it adapts an existing method to a specific domain.
The paper tackles the problem of identifying relevant skills from job descriptions by introducing Skill2vec, a neural network architecture inspired by Word2vec that transforms skills into a vector space to capture relationships, with evaluation by recruitment experts showing effectiveness.
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.