Artificial intelligence in recommender systems

Zhang, Q; Lu, J; Jin, Y

Artificial intelligence in recommender systems

Zhang, Q Lu, J

Jin, Y

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: COMPLEX & INTELLIGENT SYSTEMS, 2020
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Zhang, Q
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Jin, Y
dc.date.accessioned	2020-11-21T19:21:24Z
dc.date.available	2020-11-21T19:21:24Z
dc.date.issued	2020-11-01
dc.identifier.citation	COMPLEX & INTELLIGENT SYSTEMS, 2020
dc.identifier.issn	2199-4536
dc.identifier.issn	2198-6053
dc.identifier.uri	http://hdl.handle.net/10453/144209
dc.description.abstract	<jats:title>Abstract</jats:title> <jats:p>Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.</jats:p>
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation	http://purl.org/au-research/grants/arc/FL190100149
dc.relation.ispartof	COMPLEX & INTELLIGENT SYSTEMS
dc.relation.isbasedon	10.1007/s40747-020-00212-w
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Artificial intelligence in recommender systems
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-21T19:21:21Z
pubs.publication-status	Published

Abstract:

Abstract Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

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http://hdl.handle.net/10453/144209