Skill-based Career Path Modeling and Recommendation
Skill-based Career Path Modeling and Recommendation
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DOI：
10.1109/bigdata50022.2020.9377992
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Aritra Ghosh;B. Woolf;S. Zilberstein;Andrew S. Lan
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文献类型：
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作者：
Aritra Ghosh;B. Woolf;S. Zilberstein;Andrew S. Lan
新技术的发展正以前所未有的速度迅速改变着劳动力市场的格局。因此，对于想要建立成功职业生涯的劳动者来说，通过终身学习获得新工作所需的新技能至关重要。在本文中，我们提出了一种新颖的、可解释的单调非线性状态空间模型，用于分析在线用户的职业档案，并就如何实现他们的职业目标向用户提供可行的反馈和建议。具体地说，我们使用一系列二进制值的非递减潜在状态来表示每个用户在其职业生涯中不断扩展的技能集，并在该模型下提出了一种高效的推理方法。在两个真实世界的大型数据集上进行的一系列实验表明，我们的模型在公司、职位和技能预测等任务上的表现优于现有的方法。更重要的是，我们的模型是可解释的，可以用于其他重要任务，包括技能差距识别和职业生涯规划。通过一系列的案例研究，我们的模型可以为用户提供可操作的反馈，并指导他们完成技能提升和再技能的过程，以及ii)为用户提供实现其职业目标的可行路径建议。
The development of new technologies at an unprecedented rate is rapidly changing the landscape of the labor market. Therefore, for workers who want to build a successful career, acquiring new skills required by new jobs through lifelong learning is crucial. In this paper, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals. Specifically, we use a series of binary-valued and non-decreasing latent states to represent the expanding skill set of each user throughout their career and propose an efficient inference method under our model. Using a series of experiments on two large real-world datasets, we show that our model (sometimes significantly) outperforms existing methods on the tasks of company, job title, and skill prediction. More importantly, our model is interpretable and can be used for other important tasks including skill gap identification and career path planning. Using a series of case studies, we show that our model can provide i) actionable feedback to users and guide them through their upskilling and reskilling processes and ii) recommendations of feasible paths for users to reach their career goals.