Author ORCID Identifier

Hao Zhong https://orcid.org/0000-0001-5947-1729

Chuanren Liu https://orcid.org/0000-0001-9030-8495

Chaojiang Wu https://orcid.org/0000-0002-0047-9037

Document Type

Article

Publication Date

3-2025

DOI

https://doi.org/10.1145/3712705

Abstract

Extracting typical career paths from large-scale and unstructured talent profiles has recently attracted increasing research attention. However, various challenges arise in effectively analyzing self-reported career records. Inspired by recent advancements in neural networks and embedding models, we develop a novel career path clustering approach and apply it to uncover information technology (IT) career path patterns. Specifically, we construct employment profiles of over 60,000 IT professionals, and form their career path sequences by chaining the job records in each profile. Then we simultaneously learn cluster-wise job embeddings and construct career path clusters. The resultant cluster-wise likelihoods of career paths can quantify their soft bonding with different clusters, and the job embeddings can reveal connections among job titles within each cluster. With both real and simulated data, we conduct extensive experiments with our framework to establish the modeling performance and great improvement over the traditional optimal matching analysis methods. The empirical results from analyzing real data on career paths show that our approach can discover distinct IT career path patterns and reveal valuable insights.

Submission Type

Publisher's Version

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