Modeling user mobility for location promotion in location-based social networks

Zhu, Wen-Yuan, Peng, Wen-Chih, Chen, Ling-Jyh, Zheng, Kai and Zhou, Xiaofang (2015). Modeling user mobility for location promotion in location-based social networks. In: KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015, Sydney, NSW, Australia, (1573-1582). 10-13 August, 2015. doi:10.1145/2783258.2783331

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Author Zhu, Wen-Yuan
Peng, Wen-Chih
Chen, Ling-Jyh
Zheng, Kai
Zhou, Xiaofang
Title of paper Modeling user mobility for location promotion in location-based social networks
Conference name 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
Conference location Sydney, NSW, Australia
Conference dates 10-13 August, 2015
Convener KDD
Proceedings title KDD 2015 - 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
Series Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher Association for Computing Machinery
Publication Year 2015
Sub-type Fully published paper
DOI 10.1145/2783258.2783331
Open Access Status Not Open Access
ISBN 9781450336642
Volume 2015-August
Start page 1573
End page 1582
Total pages 10
Collection year 2016
Language eng
Abstract/Summary With the explosion of smartphones and social network services, location-based social networks (LBSNs) are increasingly seen as tools for businesses (e.g., restaurants, hotels) to promote their products and services. In this paper, we investigate the key techniques that can help businesses promote their locations by advertising wisely through the underlying LBSNs. In order to maximize the benefit of location promotion, we formalize it as an influence maximization problem in an LBSN, i.e., given a target location and an LBSN, which a set of k users (called seeds) should be advertised initially such that they can successfully propagate and attract most other users to visit the target location. Existing studies have proposed different ways to calculate the information propagation probability, that is how likely a user may influence another, in the settings of static social network. However, it is more challenging to derive the propagation probability in an LBSN since it is heavily affected by the target location and the user mobility, both of which are dynamic and query dependent. This paper proposes two user mobility models, namely Gaussian-based and distance-based mobility models, to capture the check-in behavior of individual LBSN user, based on which location-aware propagation probabilities can be derived respectively. Extensive experiments based on two real LBSN datasets have demonstrated the superior effectiveness of our proposals than existing static models of propagation probabilities to truly reflect the information propagation in LBSNs.
Keyword Check-in behavior
Influence maximization
Location-based social network
Propagation probability
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status UQ

 
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