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

Authors

Jiayin Lin, University of WollongongFollow
Geng Sun, University Of WollongongFollow
Jun Shen, University of WollongongFollow
David Pritchard
Tingru Cui, University of WollongongFollow
Dongming XuFollow
Li LiFollow
Ghassan BeydounFollow
Shiping ChenFollow

RIS ID

141018

Publication Details

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

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.