Mining Location Influence for Location Promotion in Location-Based Social Networks

With the extensive application and in-depth study of location-based social networks (LBSNs), more and more businesses are utilizing the new social platform to promote their products and services. In this paper, we explore an important technique that can help business to promote their locations by distributing items with business information or the leaflets of the business at some locations. In order to maximize the benefit of location promotion, this paper formally defines it as a location influence scope maximization problem on an location-based social network, i.e., given a target business and an LBSN, the problem is to find a set of K locations, where the business distributes small promotional gifts or leaflets on, such that people who visited these locations can successfully propagate the advertisement information to most other locations for attracting most people to visit the target location. The existing related researches neglect location influences studies on location promotion (outdoor marketing). And it is more challenging to derive the influence between locations and the spatial influence scope of each location, since location influence depends on users mobility and the target location. In this paper, we provide a new approach (called as LoP) to exploit influence between locations for location promotion. First, LoP incrementally mines correlations between locations to instruct the location-location graph (LLG). Then, LoP predicts each location's influence on others and its influence scope based on betweenness centrality. Finally, we provide the lazy algorithm to efficiently find a top-K set of influential locations for the business. Extensive experiments based on two real LBSN datasets have verified the superior effectiveness of our proposed method in term of location promotion.