Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation

Wang, Weiqing, Yin, Hongzhi, Chen, Ling, Sun, Yizhou, Sadiq, Shazia and Zhou, Xiaofang (2015). Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, (1255-1264). 10-13 August 2015. doi:10.1145/2783258.2783335

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Wang, Weiqing
Yin, Hongzhi
Chen, Ling
Sun, Yizhou
Sadiq, Shazia
Zhou, Xiaofang
Title of paper Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation
Conference name ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Conference location Sydney, NSW, Australia
Conference dates 10-13 August 2015
Convener ACM
Proceedings title Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Journal name Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Place of Publication New York, NY, United States
Publisher Association for Computing Machinery
Publication Year 2015
Sub-type Fully published paper
DOI 10.1145/2783258.2783335
Open Access Status Not Open Access
ISBN 9781450336642
Volume 2015-August
Start page 1255
End page 1264
Total pages 10
Language eng
Abstract/Summary With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important means to help people discover attractive and interesting venues and events, especially when users travel out of town. However, this recommendation is very challenging compared to the traditional recommender systems. A user can visit only a limited number of spatial items, leading to a very sparse user-item matrix. Most of the items visited by a user are located within a short distance from where he/she lives, which makes it hard to recommend items when the user travels to a far away place. Moreover, user interests and behavior patterns may vary dramatically across different geographical regions. In light of this, we propose Geo-SAGE, a geographical sparse additive generative model for spatial item recommendation in this paper. Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items. To further alleviate the data sparsity issue, Geo-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. We conduct extensive experiments and the experimental results clearly demonstrate our Geo-SAGE model outperforms the state-of-the-art.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 29 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Mon, 11 Jan 2016, 23:46:41 EST by Anthony Yeates on behalf of Scholarly Communication and Digitisation Service