research-article

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

Authors:

Xiaofang ZhouAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 3

Article No.: 48, Pages 1 - 25

https://doi.org/10.1145/3011019

Published: 20 April 2017 Publication History

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 the model 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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Cited By

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https://dl.acm.org/doi/10.1145/3477495.3531949
Song RLi TDong XDing Z(2022)An effective points of interest recommendation approach based on embedded meta‐path of spatiotemporal dataExpert Systems10.1111/exsy.1314540:2Online publication date: 21-Sep-2022
https://doi.org/10.1111/exsy.13145
Meng XDu YZhang YHan X(2022)A Survey of Context-Aware Recommender Systems: From an Evaluation PerspectiveIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3187434(1-20)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3187434
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Index Terms

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information retrieval
  2. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 3

Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers

May 2017

320 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3040485

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2017 ACM.

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Publication History

Published: 20 April 2017

Accepted: 01 October 2016

Received: 01 August 2016

Published in TIST Volume 8, Issue 3

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Cited By

Xin HLu XZhu NXu TDou DXiong HAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town UsersProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531949(1174-1184)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531949
Song RLi TDong XDing Z(2022)An effective points of interest recommendation approach based on embedded meta‐path of spatiotemporal dataExpert Systems10.1111/exsy.1314540:2Online publication date: 21-Sep-2022
https://doi.org/10.1111/exsy.13145
Meng XDu YZhang YHan X(2022)A Survey of Context-Aware Recommender Systems: From an Evaluation PerspectiveIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3187434(1-20)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3187434
Singh CSharma A(2022)A review of online supervised learningEvolving Systems10.1007/s12530-022-09448-y14:2(343-364)Online publication date: 8-Jul-2022
https://doi.org/10.1007/s12530-022-09448-y
Liu YYang ZLi TWu D(2022)A novel POI recommendation model based on joint spatiotemporal effects and four-way interactionApplied Intelligence10.1007/s10489-021-02677-952:5(5310-5324)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02677-9
Yang XLuan W(2021)A POI-Sequence Recommendation Method Based on an Exploitation-Exploration Strategy2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9659233(2518-2523)Online publication date: 17-Oct-2021
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Ficel HHaddad MBaazaoui Zghal H(2021)A graph-based recommendation approach for highly interactive platformsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115555185:COnline publication date: 15-Dec-2021
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Yin JLiu CWang WSun JHoi SGupta RLiu YShah MRajan STang JPrakash B(2020)Learning Transferrable Parameters for Long-tailed Sequential User Behavior ModelingProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403078(359-367)Online publication date: 23-Aug-2020
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