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

Lin, Jiayin; Sun, Geng; Shen, Jun; Pritchard, David; Cui, Tingru; Xu, Dongming; Li, Li; Beydoun, Ghassan; Chen, Shiping

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.