Recruitment Market Trend Analysis with Sequential Latent Variable Models

by   Chen Zhu, et al.

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.


page 2

page 6

page 9


The Future of ChatGPT-enabled Labor Market: A Preliminary Study

As a phenomenal large language model, ChatGPT has achieved unparalleled ...

Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

To cope with the accelerating pace of technological changes, talents are...

A latent variable model to measure exposure diversification in the Austrian interbank market

We propose a statistical model for weighted temporal networks capable of...

A Comprehensive Analysis of Twitter Trending Topics

Twitter is among the most used microblogging and online social networkin...

FQP 2.0: Industry Trend Analysis via Hierarchical Financial Data

Analyzing trends across industries is critical to maintaining a healthy ...

A Regularized Spatial Market Segmentation Method with Dirichlet Process Gaussian Mixture Prior

Spatially referenced data are increasingly available thanks to the devel...

High Performance Latent Variable Models

Latent variable models have accumulated a considerable amount of interes...

Please sign up or login with your details

Forgot password? Click here to reset