Inproceedings,
Guess You Like: Course Recommendation in MOOCs
Proceedings of the International Conference on Web Intelligence, page 783--789. New York, NY, USA, ACM, (2017)
DOI: 10.1145/3106426.3106478
Abstract
Recommending courses to online students is a fundamental and also challenging issue in MOOCs. Not exactly like recommendation in traditional online systems, students who enrolled the same course may have very different purposes and with very different backgrounds. For example, one may want to study "data mining" after studying the course of "big data analytics" because the former is a prerequisite course of the latter, while some other may choose "data mining" simply because of curiosity. Employing the complete data from XuetangX1, one of the largest MOOCs in China, we conduct a systematic investigation on the problem of student behavior modeling for course recommendation. We design a content-aware algorithm framework using content based users' access behaviors to extract user-specific latent information to represent students' interest profile. We also leverage the demographics and course prerequisite relation to better reveal users' potential choice. Finally, we develop a course recommendation algorithm based on the user interest, demographic profiles and course prerequisite relation using collaborative filtering strategy. Experiment results demonstrate that the proposed algorithm performs much better than several baselines (over 2X by MRR). We have deployed the recommendation algorithm onto the platform XuetangX as a new feature, which significantly helps improve the course recommendation performance (+24.6% by click rate) comparing with the recommendation strategy previously used in the system.
