SRACR: Semantic and Relationship-aware Online Course Recommendation

Abstract

With the development of educational technology, more and more people are learning and gaining ability through online courses. The excessive number of courses has brought about the problem of information overload, making it necessary to recommend suitable courses for students in online interactive way. Some traditional course recommendation schemes ignore or only roughly exploit the semantics of courses, which may result in sub-optimal recommendation. Moreover, most course recommendation schemes didn't take into account the relationships between courses (e.g., two courses are taught by a teacher, etc.). To solve the above issues, this article proposes a semantic and relationship-aware online course recommendation scheme, SRACR, to recommend favorite courses for students. Specifically, Latent Dirichlet Allocation (LDA) is used to extract the fine-grained semantics of each course represented as the topic vector of the course, and then knowledge graph embedding is adopted to map the course relationships to the course knowledge vector. Then, the course feature vector is obtained through combining the course topic vector and knowledge vector. Finally, treating the feature vector of courses as context, we use a contextual multi-armed bandit-based algorithm to estimate students' preference and recommend courses to students through balancing exploration and exploitation. The experiment results on real educational dataset demonstrate the effectiveness of our proposed method.