Facilitating course recommendations by Word2vec paradigm through social tags

Jingjing WANG, Haoran XIE, Oliver Tat Sheung AU, Lap-Kei LEE, Di ZOU, Fu Lee WANG

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

As an important educational form, online learning has attracted millions of registered learners, and a huge number of courses are available online. However, it is challenging for learners to identify appropriate courses from a large course pool due to the difficulties of mapping complex learning needs to the high-level course semantics. Several studies in the field of Natural Language Processing (NLP) have recently gained promising performance in capturing the semantic information. In this study, we use these NLP techniques to understand the semantics of learning needs and courses. Specifically, we model users’ historical course records as word sentences using skip-gram with negative sampling to obtain course semantics. Furthermore, we introduce Laplacian Eigenmaps as the objective function and integrate the course social tags and course-user interaction as penalty factors to fine-tune the course vectors, especially the courses of different categories but similar contexts. The result verifies that the proposed method is effective for recommending suitable courses for users. Copyright © 2020 Springer Nature Singapore Pte Ltd.
Original languageEnglish
Title of host publicationTechnology in education. Innovations for online teaching and learning: 5th International Conference, ICTE 2020, Macau, China, August 19-22, 2020, revised selected papers
EditorsLap-Kei LEE, Leong Hou U, Fu Lee WANG, Simon K. S. CHEUNG, Oliver AU, Kam Cheong LI
Place of PublicationSingapore
PublisherSpringer
Pages239-247
ISBN (Electronic)9789813345942
ISBN (Print)9789813345935
DOIs
Publication statusPublished - 2020

Citation

Wang, J., Xie, H., Au, O. T. S., Lee, L.-K., Zou, D., & Wang, F. L. (2020). Facilitating course recommendations by Word2vec paradigm through social tags. In L.-K. Lee, L. H. U, F. L. Wang, S. K. S. Cheung, O. Au, & K. C. Li (Eds.), Technology in education. Innovations for online teaching and learning: 5th International Conference, ICTE 2020, Macau, China, August 19-22, 2020, revised selected papers (pp. 239-247). Singapore: Springer.

Keywords

  • Recommender systems
  • Word2vec
  • Word embedding
  • Skip-gram technique

Fingerprint Dive into the research topics of 'Facilitating course recommendations by Word2vec paradigm through social tags'. Together they form a unique fingerprint.