KPCR: Knowledge Graph Enhanced Personalized Course Recommendation

Heeseok Jung, Yeonju Jang, Seonghun Kim, Hyeoncheol Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


To handle the limitations of collaborative filtering-based recommender systems, knowledge graphs are getting attention as side information. However, there are several problems to apply the existing KG-based methods to the course recommendations of MOOCs. We propose KPCR, a framework for Knowledge graph enhanced Personalized Course Recommendation. In KPCR, internal information of MOOCs and an external knowledge base are integrated through user and course related keywords. In addition, we add the level embedding module that predicts the level of students and courses. Through the experiments with the real-world datasets, we demonstrate that our knowledge graph boosts recommendation performance as side information. The results also show that the two auxiliary modules improve the recommendation performance. In addition, we evaluate the effectiveness of KPCR through the satisfaction survey of users of the real-world MOOCs platform.

Original languageEnglish
Title of host publicationAI 2021
Subtitle of host publicationAdvances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings
EditorsGuodong Long, Xinghuo Yu, Sen Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030975456
Publication statusPublished - 2022
Event34th Australasian Joint Conference on Artificial Intelligence, AI 2021 - Virtual, Online
Duration: 2022 Feb 22022 Feb 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13151 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference34th Australasian Joint Conference on Artificial Intelligence, AI 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.


  • MOOCs
  • Personalized learning
  • Recommender systems

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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