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.

本文提出了一种使用无监督学习技术，发现招聘市场趋势的新研究范式。我们开发了一种名为 MTLVM 的新颖的顺序隐变量模型，用于捕捉企业招聘状态的顺序依赖性，并能够在贝叶斯生成框架内自动学习潜在的招聘主题。最后，我们通过可视化 MTLVM 的结果，成功揭示了许多有趣的发现，例如 LBS 相关工作的流行度在 2014 年下半年达到了峰值，并在 2015 年下降。