Promoting a bundle of locations via viral marketing in location-based social networks

With the popularity of smartphones, many users utilize the check-in function to share their current activity with their friends for more social interactions in location-based social networks (LBSNs). Due to the success of viral marketing for advertising, prior works have tried to exploit viral marketing for location promotion via check-in in LBSNs. This means that k users will be selected to check in at a target location to let as many users as possible check in by the propagation of check-in. However, the prior works discuss promoting only one location at a time. This is ineffective for retail chains to promote their retail stores since they have to select k users to promote for each retail store. In this paper, we focus on selecting k users who will check in at the location in a given bundle of locations to maximize the number of users who will check in at at least one location in the given location bundle by the information propagation in an LBSN. To solve this problem, we first propose the Multi-Location-aware Independent Cascade Model (MLICM) to describe the information of a bundle of locations propagated in an LBSN. Then, we propose algorithms to effectively and efficiently select k users based on MLICM. The experimental results show that our approach outperforms than that of the state-of-the-art approaches using two real datasets.