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

Lin, J; Sun, G; Shen, J; Pritchard, D; Cui, T; Xu, D; Li, L; Beydoun, G; Chen, S

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

Lin, J Sun, G Shen, J Pritchard, D Cui, T Xu, D Li, L Beydoun, G

Chen, S

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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

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Field	Value	Language
dc.contributor.author	Lin, J
dc.contributor.author	Sun, G
dc.contributor.author	Shen, J
dc.contributor.author	Pritchard, D
dc.contributor.author	Cui, T
dc.contributor.author	Xu, D
dc.contributor.author	Li, L
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445
dc.contributor.author	Chen, S
dc.contributor.editor	Bittencourt, II
dc.contributor.editor	Cukurova, M
dc.contributor.editor	Muldner, K
dc.contributor.editor	Luckin, R
dc.contributor.editor	Millán, E
dc.date	2020-07-06
dc.date.accessioned	2021-04-14T22:44:38Z
dc.date.available	2021-04-14T22:44:38Z
dc.date.issued	2020-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12164 LNAI, pp. 168-173
dc.identifier.isbn	9783030522391
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/148115
dc.description.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.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	International Conference on Artificial Intelligence in Education
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-52240-7_31
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service
dc.type	Conference Proceeding
utslib.citation.volume	12164 LNAI
utslib.location.activity	Ifrane, Morocco,
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-14T22:44:38Z
pubs.finish-date	2020-07-10
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2020-07-06
pubs.volume	12164 LNAI
dc.location	Switzerland

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148115