Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service
| Autor: | Tingru Cui, Ghassan Beydoun, Li Li, David E. Pritchard, Jun Shen, Geng Sun, Jiayin Lin, Dongming Xu, Shiping Chen |
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| Přispěvatelé: | Bittencourt, II, Cukurova, M, Muldner, K, Luckin, R, Millán, E |
| Rok vydání: | 2020 |
| Předmět: |
Service (systems architecture)
Artificial neural network Computer science GRASP Micro learning 02 engineering and technology Recommender system Data science Article Neural network Information overload Knowledge sharing Recommendation model 020204 information systems Machine learning 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Information retrieval Artificial Intelligence & Image Processing 020201 artificial intelligence & image processing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030522391 AIED (2) Artificial Intelligence in Education |
| DOI: | 10.1007/978-3-030-52240-7_31 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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