Geographical constraint and temporal similarity modeling for point-of-interest recommendation

Wu, Huimin, Shao, Jie, Yin, Hongzhi, Shen, Heng Tao and Zhou, Xiaofang (2015). Geographical constraint and temporal similarity modeling for point-of-interest recommendation. In: Jianyong Wang, Wojciech Cellary, Dingding Wang, Hua Wang, Shu-Ching Chen, Tao Li and Yanchun Zhang, Web Information Systems Engineering – WISE 2015. International Conference on Web Information Systems Engineering, Miami, FL, United States, (426-441). 1-3 November 2015. doi:10.1007/978-3-319-26187-4_40


Author Wu, Huimin
Shao, Jie
Yin, Hongzhi
Shen, Heng Tao
Zhou, Xiaofang
Title of paper Geographical constraint and temporal similarity modeling for point-of-interest recommendation
Conference name International Conference on Web Information Systems Engineering
Conference location Miami, FL, United States
Conference dates 1-3 November 2015
Proceedings title Web Information Systems Engineering – WISE 2015   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Cham, Switzerland
Publisher Springer
Publication Year 2015
Sub-type Fully published paper
DOI 10.1007/978-3-319-26187-4_40
Open Access Status Not Open Access
ISBN 9783319261867
9783319261874
ISSN 1611-3349
Editor Jianyong Wang
Wojciech Cellary
Dingding Wang
Hua Wang
Shu-Ching Chen
Tao Li
Yanchun Zhang
Volume 9419
Start page 426
End page 441
Total pages 16
Language eng
Abstract/Summary People often share their visited Points-of-Interest (PoIs) by “check-ins”. On the one hand, human mobility varies with each individual but still implies regularity. Check-ins of an individual tend to localize in a specific geographical range. We propose a novel model to capture personalized geographical constraint of each individual. On the other hand, PoIs reflect requirements of people from different aspects. Usually, places of different functions show different temporal visiting distributions and places of similar function share similar visiting pattern in temporal aspect. Temporal distribution similarity can be used to characterize functional similarity. Based on the findings above, this paper introduces improved collaborative filtering models by jointly taking advantages of geographical constraint and temporal similarity. Experimental results on real data collected from Gowalla and JiePang demonstrate the effectiveness of our models.
Keyword Recommendation system
Collaborative filtering
Geographical constraint
Temporal similarity
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Mon, 25 Jan 2016, 20:51:10 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering