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Course recommendation of MOOC with big data support: A contextual online learning approach

机译：具有大数据支持的MOOC课程推荐：一种上下文在线学习方法

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With the advent of the big data era of MOOC, enrolled students and offered courses become numerous and diverse, resulting in a large amount of data and complex curriculum relationships. Thus how to recommend appropriate course to improve students' learning outcomes has become a daunting task. The state-of-the-art works ignore some significant features in course recommendation of MOOC: heterogeneity of large-scale user groups, sequence problem in courses and foreseeable quantitative explosion of courses and users. This paper proposes a systematic methodology for recommending personalized courses with considering the sequence of learning curriculum. The system works by recommending the course with the highest reward to a user. New feedback of the user is then recorded and will be used to improving the performance of recommendation for future students. The core component is a novel online learning algorithm based on hierarchical bandits with known smoothness. We analyze the performance of our proposed online learning algorithm in terms of regret, and prove the asymptotic optimality of the proposed algorithm. Experimental results are provided to verify our theory.
机译：随着MOOC的大数据时代的到来，在校学生和开设的课程变得越来越多样化，从而导致了大量的数据和复杂的课程关系。因此，如何推荐合适的课程来提高学生的学习成绩已成为一项艰巨的任务。最新的工作忽略了MOOC课程推荐中的一些重要功能：大规模用户群体的异质性，课程中的顺序问题以及可预见的课程和用户数量激增。本文提出了一种系统的方法，以考虑学习课程的顺序来推荐个性化课程。该系统通过向用户推荐最高奖励的课程来工作。然后记录用户的新反馈，并将其用于改进对未来学生的推荐表现。核心组件是一种基于具有已知平滑度的分层强盗的新型在线学习算法。我们从遗憾的角度分析了我们提出的在线学习算法的性能，并证明了该算法的渐近最优性。实验结果提供了验证我们的理论。

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