Career Path Clustering via Sequential Job Embedding and Mixture Markov Models

Abstract

Extracting typical career paths from large­scale and unstructured talent profiles has recently attracted increasing research attention. However, various challenges arise in effectively analyzing self­reported career records. Inspired by recent advances in neural networks and embedding models, we develop a novel career path clustering approach with two major components. First, we formulate an embedded Markov framework to learn job embeddings from longitudinal career records and further use them to compute dynamic embeddings of career paths. Second, to cope with heterogeneous career path clusters, we estimate a mixture of Markov models to optimize cluster­wise job embeddings with a prior embedded space shared by multiple clusters. We conduct extensive experiments with our framework to investigate its algorithmic performance and extract meaningful patterns of career paths in the information technology (IT) industry. The results show that our approach can naturally discover distinct career path clusters and reveal valuable insights.