TY - GEN
T1 - KPCR
T2 - 34th Australasian Joint Conference on Artificial Intelligence, AI 2021
AU - Jung, Heeseok
AU - Jang, Yeonju
AU - Kim, Seonghun
AU - Kim, Hyeoncheol
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
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.
KW - MOOCs
KW - Personalized learning
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85127102280&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-97546-3_60
DO - 10.1007/978-3-030-97546-3_60
M3 - Conference contribution
AN - SCOPUS:85127102280
SN - 9783030975456
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 739
EP - 750
BT - AI 2021
A2 - Long, Guodong
A2 - Yu, Xinghuo
A2 - Wang, Sen
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 February 2022 through 4 February 2022
ER -