KPCR: Knowledge Graph Enhanced Personalized Course Recommendation

Heeseok Jung; Yeonju Jang; Seonghun Kim; Hyeoncheol Kim

doi:10.1007/978-3-030-97546-3_60

KPCR: Knowledge Graph Enhanced Personalized Course Recommendation

Heeseok Jung
, Yeonju Jang
, Seonghun Kim
, Hyeoncheol Kim^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Citations (Scopus)

Abstract

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 language	English
Title of host publication	AI 2021
Subtitle of host publication	Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings
Editors	Guodong Long, Xinghuo Yu, Sen Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	739-750
Number of pages	12
ISBN (Print)	9783030975456
DOIs	https://doi.org/10.1007/978-3-030-97546-3_60
Publication status	Published - 2022
Event	34th Australasian Joint Conference on Artificial Intelligence, AI 2021 - Virtual, Online Duration: 2022 Feb 2 → 2022 Feb 4

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13151 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	34th Australasian Joint Conference on Artificial Intelligence, AI 2021
City	Virtual, Online
Period	22/2/2 → 22/2/4

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

MOOCs
Personalized learning
Recommender systems

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-97546-3_60

Cite this

Jung, H., Jang, Y., Kim, S., & Kim, H. (2022). KPCR: Knowledge Graph Enhanced Personalized Course Recommendation. In G. Long, X. Yu, & S. Wang (Eds.), AI 2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings (pp. 739-750). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13151 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-97546-3_60

KPCR: Knowledge Graph Enhanced Personalized Course Recommendation. / Jung, Heeseok; Jang, Yeonju; Kim, Seonghun et al.
AI 2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings. ed. / Guodong Long; Xinghuo Yu; Sen Wang. Springer Science and Business Media Deutschland GmbH, 2022. p. 739-750 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13151 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jung, H, Jang, Y, Kim, S & Kim, H 2022, KPCR: Knowledge Graph Enhanced Personalized Course Recommendation. in G Long, X Yu & S Wang (eds), AI 2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13151 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 739-750, 34th Australasian Joint Conference on Artificial Intelligence, AI 2021, Virtual, Online, 22/2/2. https://doi.org/10.1007/978-3-030-97546-3_60

Jung H, Jang Y, Kim S, Kim H. KPCR: Knowledge Graph Enhanced Personalized Course Recommendation. In Long G, Yu X, Wang S, editors, AI 2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 739-750. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-97546-3_60

Jung, Heeseok ; Jang, Yeonju ; Kim, Seonghun et al. / KPCR : Knowledge Graph Enhanced Personalized Course Recommendation. AI 2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings. editor / Guodong Long ; Xinghuo Yu ; Sen Wang. Springer Science and Business Media Deutschland GmbH, 2022. pp. 739-750 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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AB - 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.

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KPCR: Knowledge Graph Enhanced Personalized Course Recommendation

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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