Recruitment Market Trend Analysis with Sequential Latent Variable Models

TL;DR

This paper introduces MTLVM, a sequential latent variable model that leverages unsupervised learning to automatically analyze large-scale recruitment data and uncover evolving market trends over time.

Contribution

The paper presents a novel Bayesian generative model with hierarchical Dirichlet processes for dynamic recruitment trend analysis from large-scale data.

Findings

Identified peak popularity of LBS jobs in late 2014

Revealed decline in LBS jobs in 2015

Demonstrated effectiveness of MTLVM on real-world data

Abstract

Recruitment market analysis provides valuable understanding of industry-specific economic growth and plays an important role for both employers and job seekers. With the rapid development of online recruitment services, massive recruitment data have been accumulated and enable a new paradigm for recruitment market analysis. However, traditional methods for recruitment market analysis largely rely on the knowledge of domain experts and classic statistical models, which are usually too general to model large-scale dynamic recruitment data, and have difficulties to capture the fine-grained market trends. To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data. Specifically, we develop a novel sequential latent variable model, named MTLVM, which is designed for capturing the sequential dependencies of corporate recruitment states and is able to automatically learn the latent recruitment topics within a Bayesian generative framework. In particular, to capture the variability of recruitment topics over time, we design hierarchical dirichlet processes for MTLVM. These processes allow to dynamically generate the evolving recruitment topics. Finally, we implement a prototype system to empirically evaluate our approach based on real-world recruitment data in China. Indeed, by visualizing the results from MTLVM, we can successfully reveal many interesting findings, such as the popularity of LBS related jobs reached the peak in the 2nd half of 2014, and decreased in 2015.