Recommender system application developments: A survey

Lu, J; Wu, D; Mao, M; Wang, W; Zhang, G

Recommender system application developments: A survey

Lu, J

Wu, D

Mao, M Wang, W

Zhang, G

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Publication Type:: Journal Article
Citation:: Decision Support Systems, 2015, 74 pp. 12 - 32
Issue Date:: 2015-06-01

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Field	Value	Language
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Wu, D https://orcid.org/0000-0002-2499-5159	en_US
dc.contributor.author	Mao, M	en_US
dc.contributor.author	Wang, W https://orcid.org/0000-0001-8727-6090	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.available	2020-05-25T19:03:41Z
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	Decision Support Systems, 2015, 74 pp. 12 - 32	en_US
dc.identifier.issn	0167-9236	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35614
dc.description.abstract	© 2015 Elsevier B.V. A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	Decision Support Systems	en_US
dc.relation.isbasedon	10.1016/j.dss.2015.03.008	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Recommender system application developments: A survey	en_US
dc.type	Journal Article
utslib.citation.volume	74	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	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
dcterms.rights	“NOTICE: This is the author’s version of a work that was accepted for publication by Elsevier. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published by Elsevier.
pubs.publication-status	Published	en_US
pubs.volume	74	en_US

Abstract:

© 2015 Elsevier B.V. A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

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