GLP: A Novel Framework for Group-Level Location Promotion in Geo-Social Networks

Location-aware viral marketing is crucial in modern commercial applications for attracting customers to certain points of interests. Prior works are mainly based on formulating it into a location-aware influence maximization problem in Geo-social Networks (GSNs), where $K$ initial seed individuals are selected in hope of maximizing the number of final influenced users. In this paper, we present the first look into the group-level location promotion, which can potentially enhance its performance, with the phenomenon that users belonging to the same geo-community share similar moving preferences. We propose GLP, a new and novel framework of group-level location promotion by virtue of geo-communities, each of which is treated as a group in GSNs. Aiming to attract more users to designated locations, GLP firstly carries out user grouping through an iterative learning approach based on information extraction from massive check-ins records. The advantage of GLP is three-folded: i) by aggregating movements of group members, GLP significantly avoids the sparsity and sporadicity of individual check-ins, and thus obtains more reliable mobility models; ii) by generalizing a new group-level social graph, GLP can exponentially reduce the computational complexity of seed nodes selection that is algorithmically executed by a greedy algorithm; iii) in comparison with prior individual-level cases, GLP is theoretically demonstrated to drastically increase influence spread under the same given budget. Extensive experiments on real datasets demonstrate that the GLP outperforms four baselines, with notably up to 10 times larger influence spread and 100 times faster seed selection over two individual-level cases, meanwhile verifying the impact of group numbers in final influence spread.