ST-SAGE: A spatial-Temporal sparse additive generative model for spatial item recommendation

Wang, W; Yin, H; Chen, L; Sun, Y; Sadiq, S; Zhou, X

ST-SAGE: A spatial-Temporal sparse additive generative model for spatial item recommendation

Wang, W Yin, H Chen, L

Sun, Y Sadiq, S Zhou, X

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Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology, 2017, 8 (3)
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Yin, H	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Sun, Y	en_US
dc.contributor.author	Sadiq, S	en_US
dc.contributor.author	Zhou, X	en_US
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology, 2017, 8 (3)	en_US
dc.identifier.issn	2157-6904	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125111
dc.description.abstract	©c 2017 ACM 2157-6904/2017/04-ART48 15.00. With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-Temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of themodel inference algorithm on the GraphLab framework.We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-The-Art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140100545
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology	en_US
dc.relation.isbasedon	10.1145/3011019	en_US
dc.title	ST-SAGE: A spatial-Temporal sparse additive generative model for spatial item recommendation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	8	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	0806 Information Systems	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	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

©c 2017 ACM 2157-6904/2017/04-ART48 15.00. With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-Temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of themodel inference algorithm on the GraphLab framework.We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-The-Art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.

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http://hdl.handle.net/10453/125111