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Research on Content-based MOOC Recommender Model

机译：基于内容的MOOC推荐模型研究

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摘要

MOOC platform provides favorable conditions for people's lifelong learning and personalized learning. With the developments of MOOC, the supply of course resources will increase year by year and information overloading will become increasingly highlighted. At present, the major MOOC platforms provide only the classification and search functions of the courses, which are not enough. How to realize the innovative supply of courses resources and help the learners to locate the target course quickly, realizing the individualized learning, is also a question to be considered in the process of building intelligent MOOC platform. This paper, taking the platform of “iCourse”for example, proposed a content-based course recommender model for MOOC. The experiments show that the prediction precision of the proposed recommendation model is much higher than that of random recommendation and the more exact and comprehensive the descriptive data of course is, the higher the recommendation precision of the model proposed is, proving the validity of the content-based MOOC recommender model.
机译：MOOC平台为人们的终身学习和个性化学习提供了有利条件。随着MOOC的发展，课程资源的供应将逐年增加，信息过载将日益凸显。目前，主要的MOOC平台仅提供课程的分类和搜索功能，这还不够。如何实现课程资源的创新供应，帮助学习者快速定位目标课程，实现个性化学习，也是构建智能MOOC平台过程中需要考虑的问题。本文以“ iCourse”平台为例，提出了一种基于内容的MOOC课程推荐模型。实验表明，所提出的推荐模型的预测精度比随机推荐的预测精度高得多，当然描述数据越精确，全面，所提出的模型的推荐精度就越高，证明了内容的有效性。基于MOOC的推荐器模型。

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