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

*Corresponding author for this work

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


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.

Original languageEnglish
Title of host publicationTechnology in Education. Innovations for Online Teaching and Learning - 5th International Conference, ICTE 2020, Revised Selected Papers
EditorsLap-Kei LEE, Leong Hou U, Fu Lee WANG, Simon K. CHEUNG, Oliver AU, Kam Cheong LI
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9789813345935
Publication statusPublished - Dec 2020
Event5th International Conference on Technology in Education, ICTE 2020 - Macao, China
Duration: 19 Aug 202022 Aug 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference5th International Conference on Technology in Education, ICTE 2020

Bibliographical note

Funding Information:
The work described in this paper was supported by the Katie Shu Sui Pui Charitable Trust Academic Publication Fellowship (Project Reference No. KSPF2019-03).


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


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