Collaborative location recommendation by integrating multi-dimensional contextual information

Yao, L; Sheng, QZ; Wang, X; Zhang, WE; Qin, Y

Collaborative location recommendation by integrating multi-dimensional contextual information

Yao, L Sheng, QZ Wang, X

Zhang, WE Qin, Y

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Internet Technology, 2018, 18, (3)
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	Yao, L
dc.contributor.author	Sheng, QZ
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Zhang, WE
dc.contributor.author	Qin, Y
dc.date.accessioned	2022-08-29T05:34:38Z
dc.date.available	2022-08-29T05:34:38Z
dc.date.issued	2018-02-01
dc.identifier.citation	ACM Transactions on Internet Technology, 2018, 18, (3)
dc.identifier.issn	1533-5399
dc.identifier.issn	1557-6051
dc.identifier.uri	http://hdl.handle.net/10453/161028
dc.description.abstract	Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, tomodel themulti-dimensional contextual information. Tensor Factorization naturally extendsMatrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users' check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Transactions on Internet Technology
dc.relation.isbasedon	10.1145/3134438
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Networking & Telecommunications
dc.title	Collaborative location recommendation by integrating multi-dimensional contextual information
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-29T05:34:36Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	3

Abstract:

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, tomodel themulti-dimensional contextual information. Tensor Factorization naturally extendsMatrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users' check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.

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http://hdl.handle.net/10453/161028