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.
|Title of host publication||Technology in Education. Innovations for Online Teaching and Learning - 5th International Conference, ICTE 2020, Revised Selected Papers|
|Editors||Lap-Kei LEE, Leong Hou U, Fu Lee WANG, Simon K. CHEUNG, Oliver AU, Kam Cheong LI|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||9|
|Publication status||E-pub ahead of print - 17 Dec 2020|
|Event||5th International Conference on Technology in Education, ICTE 2020 - Macao, China|
Duration: 19 Aug 2020 → 22 Aug 2020
|Name||Communications in Computer and Information Science|
|Conference||5th International Conference on Technology in Education, ICTE 2020|
|Period||19/08/20 → 22/08/20|
Bibliographical noteFunding 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