Data science for next-generation recommender systems

Wang, S; Wang, Y; Sivrikaya, F; Albayrak, S; Anelli, VW

Data science for next-generation recommender systems

Wang, S

Wang, Y Sivrikaya, F Albayrak, S Anelli, VW

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Publisher:: SPRINGERNATURE
Publication Type:: Journal Article
Citation:: International Journal of Data Science and Analytics, 2023, 16, (2), pp. 135-145
Issue Date:: 2023-08-01

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Field	Value	Language
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Wang, Y
dc.contributor.author	Sivrikaya, F
dc.contributor.author	Albayrak, S
dc.contributor.author	Anelli, VW
dc.date.accessioned	2023-10-31T23:35:58Z
dc.date.available	2023-10-31T23:35:58Z
dc.date.issued	2023-08-01
dc.identifier.citation	International Journal of Data Science and Analytics, 2023, 16, (2), pp. 135-145
dc.identifier.issn	2364-415X
dc.identifier.issn	2364-4168
dc.identifier.uri	http://hdl.handle.net/10453/172914
dc.description.abstract	Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.
dc.language	English
dc.publisher	SPRINGERNATURE
dc.relation.ispartof	International Journal of Data Science and Analytics
dc.relation.isbasedon	10.1007/s41060-023-00404-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	4605 Data management and data science
dc.title	Data science for next-generation recommender systems
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-10-31T23:35:57Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	2

Abstract:

Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.

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http://hdl.handle.net/10453/172914