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Inf. Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>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 Self-explaining Skill Recommendation Deep Q-network (SeSRDQN) that captures and prototypes prevalent skill sets in the market into representative exemplars for decision-making. SeSRDQN quantitatively decomposes the talent\u2019s long-term learning utility into contributions from each exemplar, offering a comprehensive and multi-factorial 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.<\/jats:p>","DOI":"10.1145\/3704998","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T17:52:59Z","timestamp":1732211579000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4763-6060","authenticated-orcid":false,"given":"Ying","family":"Sun","sequence":"first","affiliation":[{"name":"Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology\u2014Guangzhou Campus, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6867-8715","authenticated-orcid":false,"given":"Yang","family":"Ji","sequence":"additional","affiliation":[{"name":"Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology\u2014Guangzhou Campus, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-7009","authenticated-orcid":false,"given":"Fuzhen","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8833-5398","authenticated-orcid":false,"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong and Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology\u2014Guangzhou Campus, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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