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

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12164 LNAI, pp. 168-173
Issue Date:
2020-01-01
Filename Description Size
Lin2020_Chapter_Deep-Cross-AttentionRecommenda.pdfPublished version151.36 kB
Adobe PDF
Full metadata record
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.
Please use this identifier to cite or link to this item: