A heuristic approach for new-item cold start problem in recommendation of micro open education resources

Sun, G; Cui, T; Xu, D; Shen, J; Chen, S

A heuristic approach for new-item cold start problem in recommendation of micro open education resources

Sun, G Cui, T Xu, D Shen, J

Chen, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10858 LNCS pp. 212 - 222
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Sun, G	en_US
dc.contributor.author	Cui, T	en_US
dc.contributor.author	Xu, D	en_US
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140	en_US
dc.contributor.author	Chen, S	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10858 LNCS pp. 212 - 222	en_US
dc.identifier.isbn	9783319914633	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131252
dc.description.abstract	© Springer International Publishing AG, part of Springer Nature 2018. The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-91464-0_21	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A heuristic approach for new-item cold start problem in recommendation of micro open education resources	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10858 LNCS	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	10858 LNCS	en_US

Abstract:

© Springer International Publishing AG, part of Springer Nature 2018. The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies.

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http://hdl.handle.net/10453/131252