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首页> 外文会议>International conference on knowledge science, engineering and management >MOOCRec: An Attention Meta-path Based Model for Top-K Recommendation in MOOC
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MOOCRec: An Attention Meta-path Based Model for Top-K Recommendation in MOOC

机译：MOOCRec：MOOC中基于注意元路径的Top-K推荐模型

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摘要

With the surge of the courses and users on Massive Open Online Courses (MOOC), MOOC has accumulated rich educational data. However, the utilization of MOOC resources is not high enough to satisfy the dynamic and diverse demands of different individuals. Meanwhile, the traditional recommendation model for MOOC dataset underperforms in both precision and recall. To address those issues, we collect and collate a MOOC dataset and then propose an attention meta-path based recommendation model named MOOCRec to jointly learn explicit and implicit relationships between students and courses. By extracting the knowledge points of the whole course information, we successfully construct different heterogeneous information networks (HINs) in MOOC and then we elaborately design multiple meta-paths based context to exploit the heterogeneity of other HINs in MOOC, which enables MOOCRec to offer abundant course resources. In particular, we leverage three attention mechanisms under MOOC to further enhance factors that effectively influence student preferences to improve the precision of our model. What's more, we adopt another classical dataset called Movielens, reconstruct HINs and redesign meta-paths to demonstrate that the extensive availability of MOOCRec.
机译：随着大量课程和在线公开课程（MOOC）的用户激增，MOOC积累了丰富的教育数据。但是，MOOC资源的利用程度不足以满足不同个人的动态和多样化需求。同时，MOOC数据集的传统推荐模型在准确性和召回率方面均表现不佳。为了解决这些问题，我们收集并整理了一个MOOC数据集，然后提出了一个基于关注元路径的推荐模型MOOCRec，以共同学习学生与课程之间的显式和隐式关系。通过提取整个课程信息的知识点，我们成功地在MOOC中构建了不同的异构信息网络（HIN），然后我们精心设计了基于元路径的上下文，以利用MOOC中其他HIN的异构性，从而使MOOCRec能够提供丰富的信息。课程资源。特别是，我们利用MOOC下的三种注意力机制来进一步增强有效影响学生偏好的因素，从而提高模型的准确性。此外，我们采用了另一个称为Movielens的经典数据集，重构了HIN并重新设计了元路径，以证明MOOCRec的广泛可用性。

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