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Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Journal Article


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Abstract


  • Aims to provide flexible, effective and personalized online learning service, micro learning has gained wide attention in recent years as more people turn to use fragment time to grasp fragmented knowledge. Widely available online knowledge sharing is one of the most representative approaches to micro learning, and it is well accepted by online learners. However, information overload challenges such personalized online learning services. In this paper, we propose a deep cross attention recommendation model to provide online users with personalized resources based on users’ profile and historical online behaviours. This model benefits from the deep neural network, feature crossing, and attention mechanism mutually. The experiment result showed that the proposed model outperformed the state-of-the-art baselines.

UOW Authors


  •   Lin, Jiayin (external author)
  •   Sun, Geng (external author)
  •   Shen, Jun
  •   Pritchard, David (external author)
  •   Cui, Tingru
  •   Xu, Dongming (external author)
  •   Li, Li (external author)
  •   Beydoun, Ghassan (external author)
  •   Chen, Shiping (external author)

Publication Date


  • 2020

Citation


  • Lin, J., Sun, G., Shen, J., Pritchard, D., Cui, T., Xu, D., Li, L., Beydoun, G. & Chen, S. (2020). Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service. Lecture notes in computer science, 12164 168-173. International Conference on Artificial Intelligence in Education

Ro Full-text Url


  • https://ro.uow.edu.au/context/eispapers1/article/5194/type/native/viewcontent

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4167

Number Of Pages


  • 5

Start Page


  • 168

End Page


  • 173

Volume


  • 12164

Place Of Publication


  • Germany

Abstract


  • Aims to provide flexible, effective and personalized online learning service, micro learning has gained wide attention in recent years as more people turn to use fragment time to grasp fragmented knowledge. Widely available online knowledge sharing is one of the most representative approaches to micro learning, and it is well accepted by online learners. However, information overload challenges such personalized online learning services. In this paper, we propose a deep cross attention recommendation model to provide online users with personalized resources based on users’ profile and historical online behaviours. This model benefits from the deep neural network, feature crossing, and attention mechanism mutually. The experiment result showed that the proposed model outperformed the state-of-the-art baselines.

UOW Authors


  •   Lin, Jiayin (external author)
  •   Sun, Geng (external author)
  •   Shen, Jun
  •   Pritchard, David (external author)
  •   Cui, Tingru
  •   Xu, Dongming (external author)
  •   Li, Li (external author)
  •   Beydoun, Ghassan (external author)
  •   Chen, Shiping (external author)

Publication Date


  • 2020

Citation


  • Lin, J., Sun, G., Shen, J., Pritchard, D., Cui, T., Xu, D., Li, L., Beydoun, G. & Chen, S. (2020). Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service. Lecture notes in computer science, 12164 168-173. International Conference on Artificial Intelligence in Education

Ro Full-text Url


  • https://ro.uow.edu.au/context/eispapers1/article/5194/type/native/viewcontent

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4167

Number Of Pages


  • 5

Start Page


  • 168

End Page


  • 173

Volume


  • 12164

Place Of Publication


  • Germany