ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

Wang, Weiqing , Yin, Hongzhi , Chen, Ling , Sun, Yizhou , Sadiq, Shazia and Zhou, Xiaofang (2017) ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation. ACM Transactions on Intelligent Systems and Technology, 8 3: 48.1-48.25. doi:10.1145/3011019


Author Wang, Weiqing
Yin, Hongzhi
Chen, Ling
Sun, Yizhou
Sadiq, Shazia
Zhou, Xiaofang
Title ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation
Journal name ACM Transactions on Intelligent Systems and Technology   Check publisher's open access policy
ISSN 2157-6904
2157-6912
Publication date 2017-04-22
Sub-type Article (original research)
DOI 10.1145/3011019
Open Access Status Not yet assessed
Volume 8
Issue 3
Start page 48.1
End page 48.25
Total pages 25
Place of publication New York, NY, United States
Publisher Association for Computing Machinery
Language eng
Subject 2614 Theoretical Computer Science
1702 Artificial Intelligence
Abstract 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.
Keyword Point of interest (POI)
Real-time recommendation
Location-based service
Online learning
Efficient retrieval algorithm
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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Created: Wed, 13 Sep 2017, 09:42:58 EST by Weiqing Wang on behalf of School of Information Technol and Elec Engineering