@inproceedings{b584cb6e69b14ca3967e8a3b910e893f,
title = "Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model",
abstract = "Career mobility forecasting and recommendation are important topics in talent management research. While existing models have extensively covered short-term, single-period recommendations and long-term, unconstrained career path suggestions, the user preference-constrained career path optimization problem remains underexplored. This paper addresses the common scenario where individuals have approximate career plans and seek to optimize their career trajectories by incorporating specific user preferences. We develop an exploration space-aware stochastic searching algorithm that incorporates a deep learning-guided searching space determination module and a position transit prediction module. We mathematically demonstrate its strengths in exploring optimal path solutions with fixed components predefined by users. Finally, we empirically validate the superiority of our method using a comprehensive real-world dataset, comparing it against state-of-the-art approaches.",
keywords = "career mobility, career path recommendation, deep learning, sequential recommendation, simulated annealing",
author = "Pengzhan Guo and Keli Xiao and Hengshu Zhu and Qingxin Meng",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining, ICDM 2023 ; Conference date: 01-12-2023 Through 04-12-2023",
year = "2023",
doi = "10.1109/ICDM58522.2023.00021",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "120--129",
editor = "Guihai Chen and Latifur Khan and Xiaofeng Gao and Meikang Qiu and Witold Pedrycz and Xindong Wu",
booktitle = "Proceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023",
address = "United States",
}