A skill's value depends on dynamic market conditions. To remain marketable, contractors need to keep reskilling themselves continuously. But choosing new skills to learn is an inherently hard task: Contractors have very little information about current and future market conditions, which often results in poor learning choices. Recommendation frameworks could reduce uncertainty in learning choices. However, conventional approaches would likely be inefficient; they would model previous (often poor) observed contractor learning behaviors to provide future career path recommendations while ignoring current market trends. This work proposes a framework that combines reinforcement learning, Bayesian inference, and gradient boosting to provide recommendations on how contractors should behave when choosing new skills to learn. Compared with standard recommender systems, this framework does not learn from previous (often poor) behaviors to make future recommendations. Instead, it relies on a Markov Decision Process to operate on a graph of feasible actions and dynamically recommend profitable career paths. The framework uses market information to identify current trends and project future wages. Based on this information, it recommends feasible, relevant actions that a contractor can take to learn new, in-demand skills. Evaluation of the framework on 1.73 million job applications from an online labor market shows that its implementation could increase (1) the marketplace's revenue by up to 6%, (2) contractors' wages by 22%, and (3) the diversity of new skill acquisitions by 47%. A comparison with alternative recommender systems highlights the limitations of approaches that make recommendations based on previously observed learning behaviors.

中文翻译：

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需求感知职业路径建议：强化学习方法

技能的价值取决于动态的市场条件。为了保持适销对路，承包商需要不断提高自己的技能。但是，选择新的学习技能本质上是一项艰巨的任务：承包商对当前和未来的市场状况知之甚少，这常常导致学习选择不佳。建议框架可以减少学习选择中的不确定性。但是，常规方法可能效率不高；他们将对以前（通常是较差）观察到的承包商学习行为进行建模，以提供未来的职业道路建议，同时忽略当前的市场趋势。这项工作提出了一个框架，该框架结合了强化学习，贝叶斯推理和梯度提升，可为承包商在选择新的学习技能时应如何表现提供建议。与标准推荐器系统相比，此框架不会从以前的（通常是不良的）行为中汲取教训来提出未来的建议。取而代之的是，它依靠马尔可夫决策过程在可行的行动图上进行操作，并动态推荐可获利的职业道路。该框架使用市场信息来识别当前趋势并预测未来的工资。根据这些信息，它建议承包商可以采取可行的相关措施来学习新的需求技能。对在线劳动力市场上173万份工作申请的框架的评估表明，该框架的实施可能会增加（1）市场收入增长6％，（2）承包商的工资增长22％，以及（3）新的多样性技能获得率提高了47％。