Adaptive talent journey: Optimization of talents’ growth path within a company via Deep Q-Learning
作者：
Highlights：
• Definition of a novel method for optimizing the growth path of talent in a company.
• The method provides talents with adequate learning experiences to acquire skills.
• The method, namely Adaptive Talent Journey, is based on Reinforcement Learning.
• The method considers current talent’s skills, learning style, and personal traits.
• We have evaluated effectiveness of Adaptive Talent Journey and users satisfaction.
摘要
•Definition of a novel method for optimizing the growth path of talent in a company.•The method provides talents with adequate learning experiences to acquire skills.•The method, namely Adaptive Talent Journey, is based on Reinforcement Learning.•The method considers current talent’s skills, learning style, and personal traits.•We have evaluated effectiveness of Adaptive Talent Journey and users satisfaction.
论文关键词：Talent journey,Deep Q-Learning,Digital Twin,User evaluation
论文评审过程：Received 17 March 2022, Revised 8 July 2022, Accepted 25 July 2022, Available online 30 July 2022, Version of Record 8 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118302
