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Inf. Syst."],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>\n            As a major component of strategic talent management, learning and development (L&amp;D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this article, we present a focused study on the explainable personalized online course recommender system for enhancing employee training and development. Specifically, we first propose a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN), to jointly model both the employees\u2019 current competencies and their career development preferences in an explainable way. In DCBVN, we first extract the latent interpretable representations of the employees\u2019 competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Furthermore, for handling the employees with sparse or missing skill profiles, we develop an improved version of DCBVN, called the\n            <jats:italic>Demand-aware Collaborative Competency Attentive Network (DCCAN) framework<\/jats:italic>\n            , by considering the connectivity among employees. In DCCAN, we first build two employee competency graphs from learning and working aspects. Then, we design a graph-attentive network and a multi-head integration mechanism to infer one\u2019s competency information from her neighborhood employees. Finally, we can generate explainable recommendation results based on the competency representations. Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of both of our frameworks, as well as their robustness on sparse and cold-start scenarios.\n          <\/jats:p>","DOI":"10.1145\/3490476","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T20:11:53Z","timestamp":1638994313000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach"],"prefix":"10.1145","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7717-447X","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Baidu Talent Intelligence Center, Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu Talent Intelligence Center, Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Thrust, The Hong Kong University of Science and Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/1283383.1283494"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the AAAI Workshop on Educational Data Mining","volume":"3","author":"Bendakir Narimel","year":"2006","unstructured":"Narimel Bendakir and Esma A\u00efmeur. 2006. 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