Attention-Based CNN for Personalized Course Recommendations for MOOC Learners

Jingjing WANG, Haoran XIE, Oliver Tat Sheung AU, Di ZOU, Fu Lee WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

Abstract

Massive Open Online Courses (MOOCs), which are open for anyone without limitations on time or location, have attracted millions of registered online students. The large number of online courses available raises the question of how appropriate courses can be effectively recommended to interested learners. The recommendation system, widely used in various online applications, is a good solution for reducing decision complexity. In this paper, we propose the method of using attention-based convolutional neural networks (CNN) to obtain a user's profile, predict the user ratings, and recommend the top-n courses. First, we represent the learner behaviors and learning histories into feature vectors. The attention mechanism is then used to improve relevance estimation according to the differences between the estimation scores and the actual scores given by users to train the neural network. Finally, the trained model will recommend courses to learners. At the end of the paper, we introduce the framework of our system.
Original languageEnglish
Title of host publicationProceedings of 2020 International Symposium on Educational Technology (ISET)
PublisherIEEE
Pages180-184
Number of pages5
ISBN (Electronic)9781728171890
DOIs
Publication statusPublished - Aug 2020
Event2020 International Symposium on Educational Technology (ISET) - Bangkok, Thailand
Duration: 24 Aug 202027 Aug 2020

Conference

Conference2020 International Symposium on Educational Technology (ISET)
Period24/08/2027/08/20

Keywords

  • neural network
  • attention mechanism
  • course recommendation

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    WANG, J., XIE, H., AU, O. T. S., ZOU, D., & WANG, F. L. (2020). Attention-Based CNN for Personalized Course Recommendations for MOOC Learners. In Proceedings of 2020 International Symposium on Educational Technology (ISET) (pp. 180-184). IEEE. https://doi.org/10.1109/ISET49818.2020.00047