TY - JOUR
T1 - Exploiting viral marketing for location promotion in location-based social networks
AU - Zhu, Wen Yuan
AU - Peng, Wen Chih
AU - Chen, Ling Jyh
AU - Zheng, Kai
AU - Zhou, Xiaofang
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/11
Y1 - 2016/11
N2 - With the explosion of smartphones and social network services, location-based social networks (LBSNs) are increasingly seen as tools for businesses (e.g., restaurants and hotels) to promote their products and services. In this article, 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, a set of k users (called seeds) should be advertised initially such that they can successfully propagate and attract many other users to visit the target location. Existing studies have proposed different ways to calculate the information propagation probability, that is, how likely it is that a user may influence another, in the setting of a 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 article proposes two user mobility models, namely the Gaussian-based and distance-based mobility models, to capture the check-in behavior of individual LBSN users, based on which location-aware propagation probabilities can be derived. Extensive experiments based on two real LBSN datasets have demonstrated the superior effectiveness of our proposals compared with existing static models of propagation probabilities to truly reflect the information propagation in LBSNs.
AB - With the explosion of smartphones and social network services, location-based social networks (LBSNs) are increasingly seen as tools for businesses (e.g., restaurants and hotels) to promote their products and services. In this article, 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, a set of k users (called seeds) should be advertised initially such that they can successfully propagate and attract many other users to visit the target location. Existing studies have proposed different ways to calculate the information propagation probability, that is, how likely it is that a user may influence another, in the setting of a 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 article proposes two user mobility models, namely the Gaussian-based and distance-based mobility models, to capture the check-in behavior of individual LBSN users, based on which location-aware propagation probabilities can be derived. Extensive experiments based on two real LBSN datasets have demonstrated the superior effectiveness of our proposals compared with existing static models of propagation probabilities to truly reflect the information propagation in LBSNs.
KW - Check-in behavior
KW - Influence maximization
KW - Location-based social network
KW - Propagation probability
UR - http://www.scopus.com/inward/record.url?scp=84997418638&partnerID=8YFLogxK
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U2 - 10.1145/3001938
DO - 10.1145/3001938
M3 - Article
AN - SCOPUS:84997418638
SN - 1556-4681
VL - 11
JO - ACM Transactions on Knowledge Discovery from Data
JF - ACM Transactions on Knowledge Discovery from Data
IS - 2
M1 - 25
ER -