Zhongying Zhao
Abstract
Massive Open Online Courses, offering millions of high-quality courses from prestigious universities and prominent experts, are picking up momentum in popularity. Although users enrolling on MOOCs have free access to abundant knowledge, they may easily get overwhelmed by information overload. Therefore, there is a need of recommending technology as a fundamental and well-accepted effective solution. However, differing from many other online recommendations, recommending courses to users on MOOCs faces two challenges. First, users’ knowledge background differs, so does their purpose of learning. Second, online courses are not independent but intertwined with prerequisite relations. Therefore, it is necessary to take these two challenges into account when designing a recommending method. To tackle this issue, in this article, we first propose two algorithms for extracting concept-level and course-level prerequisite relations. We then present the recommending method GuessUNeed based on neural attention network and course prerequisite relation embeddings. The experimental results on real-world datasets demonstrate the superiority of the proposed GuessUNeed method.
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Index Terms
- GuessUNeed: Recommending Courses via Neural Attention Network and Course Prerequisite Relation Embeddings
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