Recommending Learning Objects through Attentive Heterogeneous Graph Convolution and Operation- Aware Neural Network (Extended Abstract)

Paper

Zhu, Yifan, Lin, Qika, Lu, Hao, Shi, Kaize, Liu, Donglei, Chambua, James, Wang, Shanshan and Niu, Zhendong. 2024. "Recommending Learning Objects through Attentive Heterogeneous Graph Convolution and Operation- Aware Neural Network (Extended Abstract)." 2024 IEEE 40th International Conference on Data Engineering (ICDE). Utrecht, Netherlands 13 - 14 May 2024 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICDE60146.2024.00505

Paper/Presentation Title	Recommending Learning Objects through Attentive Heterogeneous Graph Convolution and Operation- Aware Neural Network (Extended Abstract)
Presentation Type	Paper
Authors	Zhu, Yifan, Lin, Qika, Lu, Hao, Shi, Kaize, Liu, Donglei, Chambua, James, Wang, Shanshan and Niu, Zhendong
Journal or Proceedings Title	Proceedings of 2024 IEEE 40th International Conference on Data Engineering (ICDE)
Journal Citation	pp. 5747-5748
Number of Pages	2
Year	2024
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Place of Publication	United States
ISBN	9798350317152
	9798350317169
Digital Object Identifier (DOI)	https://doi.org/10.1109/ICDE60146.2024.00505
Web Address (URL) of Paper	https://ieeexplore.ieee.org/document/10598126
Web Address (URL) of Conference Proceedings	https://ieeexplore.ieee.org/xpl/conhome/10597630/proceeding
Conference/Event	2024 IEEE 40th International Conference on Data Engineering (ICDE)
Event Details	2024 IEEE 40th International Conference on Data Engineering (ICDE) Parent International Conference on Data Engineering Delivery In person Event Date 13 to end of 14 May 2024 Event Location Utrecht, Netherlands
Abstract	Currently, the increasing information overload on Massive Open Online Courses(MOOCs) inhibits the appropriate choice of learning objects by learners, leading to low efficiency and high dropout rates. However, in MOOC platforms, recommendation network structures that can selectively extract implicit features such as heterogeneous learning preference and knowledge organization of learning objects are still not comprehensively studied. To this end, we propose a learning object recommendation model namely ACGCN based on heterogeneous learning behavior and knowledge graph. By introducing an attention mechanism, information is amplified when updating the representation of the heterogeneous graph, which eliminates the impact of noise and improves the robustness of ACGCN. Experimental results using a real-world dataset revealed that our proposed model has the best performance compared to those of several existing baselines.
Keywords	Learning Objects Recommendation; Heterogeneous Graph Net
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	4602. Artificial intelligence
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	Beijing University of Posts and Telecommunications, China
	National University of Singapore
	Chinese Academy of Sciences, China
	University of Technology Sydney
	Beijing Institute of Technology, China
	University of Dar es Salaam, Tanzania
	Beijing University of Civil Engineering and Architecture, China

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