TY - GEN
T1 - Recommending Learning Objects through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network (Extended Abstract)
AU - Zhu, Yifan
AU - Lin, Qika
AU - Lu, Hao
AU - Shi, Kaize
AU - Liu, Donglei
AU - Chambua, James
AU - Wan, Shanshan
AU - Niu, Zhendong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Heterogeneous Graph Network
KW - Learning Objects Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85200516826&partnerID=8YFLogxK
U2 - 10.1109/ICDE60146.2024.00505
DO - 10.1109/ICDE60146.2024.00505
M3 - Conference contribution
AN - SCOPUS:85200516826
T3 - Proceedings - International Conference on Data Engineering
SP - 5747
EP - 5748
BT - Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
PB - IEEE Computer Society
T2 - 40th IEEE International Conference on Data Engineering, ICDE 2024
Y2 - 13 May 2024 through 17 May 2024
ER -