A fuzzy preference tree-based recommender system for personalized business-to-business e-services

Wu, D; Zhang, G; Lu, J

A fuzzy preference tree-based recommender system for personalized business-to-business e-services

Wu, D

Zhang, G

Lu, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2015, 23 (1), pp. 29 - 43
Issue Date:: 2015-02-01

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Field	Value	Language
dc.contributor.author	Wu, D https://orcid.org/0000-0002-2499-5159	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2015-02-01	en_US
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2015, 23 (1), pp. 29 - 43	en_US
dc.identifier.issn	1063-6706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35618
dc.description.abstract	© 2014 IEEE. The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businesses or individuals). Although recommender systems have been well studied, there are still two challenges in the development of a recommender system, particularly in real-world B2B e-services: 1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and 2) online users' preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key technique in this study is a comprehensive tree matching method, which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business dataset and the MovieLens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially in e-business applications. This study also applies the proposed recommendation approach to the development of a web-based business partner recommender system.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/TFUZZ.2014.2315655	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A fuzzy preference tree-based recommender system for personalized business-to-business e-services	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	23	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

© 2014 IEEE. The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businesses or individuals). Although recommender systems have been well studied, there are still two challenges in the development of a recommender system, particularly in real-world B2B e-services: 1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and 2) online users' preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key technique in this study is a comprehensive tree matching method, which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business dataset and the MovieLens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially in e-business applications. This study also applies the proposed recommendation approach to the development of a web-based business partner recommender system.

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http://hdl.handle.net/10453/35618