Hierarchical Job Classification with Similarity Graph Integration

TL;DR

This paper introduces a novel hierarchical job classification model that embeds jobs and industry categories into a latent space, leveraging graph and hierarchical relationships to improve accuracy in online recruitment systems.

Contribution

The paper presents a new representation learning model that integrates hierarchical industry data and graph structures, addressing limitations of traditional text classification methods.

Findings

Significantly outperforms existing classification methods on large-scale datasets.

Effectively captures hierarchical and semantic relationships in job data.

Enhances cold start handling and dynamic candidate-job matching.

Abstract

In the dynamic realm of online recruitment, accurate job classification is paramount for optimizing job recommendation systems, search rankings, and labor market analyses. As job markets evolve, the increasing complexity of job titles and descriptions necessitates sophisticated models that can effectively leverage intricate relationships within job data. Traditional text classification methods often fall short, particularly due to their inability to fully utilize the hierarchical nature of industry categories. To address these limitations, we propose a novel representation learning and classification model that embeds jobs and hierarchical industry categories into a latent embedding space. Our model integrates the Standard Occupational Classification (SOC) system and an in-house hierarchical taxonomy, Carotene, to capture both graph and hierarchical relationships, thereby improving classification accuracy. By embedding hierarchical industry categories into a shared latent space, we tackle cold start issues and enhance the dynamic matching of candidates to job opportunities. Extensive experimentation on a large-scale dataset of job postings demonstrates the model's superior ability to leverage hierarchical structures and rich semantic features, significantly outperforming existing methods. This research provides a robust framework for improving job classification accuracy, supporting more informed decision-making in the recruitment industry.