Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning

期限(时间) 强化学习 钢筋 就业市场 计算机科学 人工智能 心理学 工程类 社会心理学 工作(物理) 机械工程 物理 量子力学
作者
Ying Sun,Yang Ji,Hengshu Zhu,Fuzhen Zhuang,Qing He,Hui Xiong
出处
期刊:ACM Transactions on Information Systems [Association for Computing Machinery]
标识
DOI:10.1145/3704998
摘要

Continuously learning new skills is essential for talents to gain a competitive advantage in the labor market. Despite extensive efforts on relevance- or preference-based skill recommendations, little attention has been given to the practical effects of job skills in the market. To bridge this gap, we propose an explainable personalized skill learning recommendation system that considers the long-term learning benefits and costs. Specifically, we model skill learning utilities based on salary and learning cost associated with job positions and propose a multi-objective deep reinforcement learning framework to model and maximize long-term utilities. Furthermore, we propose a S elf- E xplaining S kill R ecommendation D eep Q-N etwork (SeSRDQN) that captures and prototypes prevalent skill sets in the market into representative exemplars for decision-making. SeSRDQN quantitatively decomposes the talent’s long-term learning utility into contributions from each exemplar, offering a comprehensive and multifactorial explanation across various skill learning options. To tackle the combinatorial complexity of the skill space, we develop an MCTS-based optimization-decoding iterative training procedure for explanation fidelity and human understandability. In this way, talents will receive a tailored roadmap of essential skills, complemented by exemplar-based explanations, to effectively plan their careers. Extensive experiments on a real-world dataset validate the effectiveness and explainability of our approach.
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