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

Yifan Zhu, Qika Lin, Hao Lu, Kaize Shi, Donglei Liu, James Chambua, Shanshan Wan, Zhendong Niu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
PublisherIEEE Computer Society
Pages5747-5748
Number of pages2
ISBN (Electronic)9798350317152
DOIs
Publication statusPublished - 2024
Event40th IEEE International Conference on Data Engineering, ICDE 2024 - Utrecht, Netherlands
Duration: 13 May 202417 May 2024

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627
ISSN (Electronic)2375-0286

Conference

Conference40th IEEE International Conference on Data Engineering, ICDE 2024
Country/TerritoryNetherlands
CityUtrecht
Period13/05/2417/05/24

Keywords

  • Heterogeneous Graph Network
  • Learning Objects Recommendation

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