A hybrid recommendation approach for hierarchical items

Wu, D; Lu, J; Zhang, G

A hybrid recommendation approach for hierarchical items

Wu, D

Lu, J

Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, 2010, pp. 492 - 497
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Wu, D https://orcid.org/0000-0002-2499-5159	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, 2010, pp. 492 - 497	en_US
dc.identifier.isbn	9781424467907	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16302
dc.description.abstract	Recommender systems aim to recommend items that are likely to be of interest to the user. In many business situations, complex items are described by hierarchical tree structures, which contain rich semantic information. To recommend hierarchical items accurately, the semantic information of the hierarchical tree structures must be considered comprehensively. In this study, a new hybrid recommendation approach for complex hierarchical tree structured items is proposed. In this approach, a comprehensive semantic similarity measure model for hierarchical tree structured items is developed. It is integrated with the traditional item-based collaborative filtering approach to generate recommendations. © 2010 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0880739
dc.relation.ispartof	Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010	en_US
dc.relation.isbasedon	10.1109/ISKE.2010.5680827	en_US
dc.title	A hybrid recommendation approach for hierarchical items	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hangzhou, China	en_US
dc.location.activity	ISI:000178570500011
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Recommender systems aim to recommend items that are likely to be of interest to the user. In many business situations, complex items are described by hierarchical tree structures, which contain rich semantic information. To recommend hierarchical items accurately, the semantic information of the hierarchical tree structures must be considered comprehensively. In this study, a new hybrid recommendation approach for complex hierarchical tree structured items is proposed. In this approach, a comprehensive semantic similarity measure model for hierarchical tree structured items is developed. It is integrated with the traditional item-based collaborative filtering approach to generate recommendations. © 2010 IEEE.

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http://hdl.handle.net/10453/16302