Learning job representation using directed graph embedding
Haiyan Luo, Shichuan Ma, Anand Joseph Bernard Selvaraj, Yu Sun · 2019
In recent years, embedding technologies have gained popularity in many areas of machine learning, such as NLP, computer vision, information retrieval, etc.. In this paper, we propose a latent representation of job positions consisting of job title and company pairs, which can capture not only similarity relations but also ordering relations among job positions. We first construct a directed graph of job positions from the user's job transition history in the resume data, then we train the job position embedding using an asymmetric relation preserving graph embedding algorithm. Experimental results on a career move prediction task using real-world data set demonstrated that the proposed embedding solution can outperform state-of-the-art embedding methods.