Abstract
In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users, less effort is made for identifying propagators in LBSN. In this work, we propose and tackle two novel tasks, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of Location Promotion. Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l in the future. To deal with TPD and TCD, we propose a novel graph embedding method, LBSN2vec. The main idea is to jointly learn a low dimensional feature representation for each user and each location in an LBSN. Equipped with learned embedding vectors, we propose two similarity-based measures, Influential and Visiting scores, to find potential targeted propagators and customers. Experiments conducted on a large-scale Instagram LBSN dataset exhibit that LBSN2vec and its variant can significantly outperform well-known network embedding methods in both tasks.
Highlights
In recent years, location check-ins at Points-of-Interests (POIs) on social media has become a living habit
We find that GraphGAN has the worst performance, as it cannot capture the contagion between users and POIs
We think the superiority of our methods comes from the derived embedding space that better utilizes the information between user–user interactions and user-POI visiting records in location-based social networks, which is crucial in modeling the potential of future POI visiting
Summary
Location check-ins at Points-of-Interests (POIs) on social media has become a living habit. People love sharing their life on Facebook or Instagram. Location promotion is one of the essential problems in LBSN [10,11], and the basic goal is to find the set of seeds (i.e., information propagators) to maximize the number of users (i.e., customers) to visit the target POI [10,11,12,13,14]. It is usually expected that the identified propagators can truly generate more users to visit the target POI (i.e., more customers) in the future, as discussed in existing studies [10,11,15]. In other words, existing location promotion lacks predictability when finding propagators and customers
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