###### 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, LBSN2 vec. 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 LBSN2 vec and its variant can significantly outperform well-known network embedding methods in both tasks. location-based social networks; promotion propagators; potential customer; location promotion; embedding learning +
Footnote †: journal: Medical  
To consider the predictability in location promotion, this paper proposes to solve two novel problems. The first is _Targeted Propagator Discovery_ (TPD) while the second is _Targeted Customer Discovery_ (TCD). The TPD problem is that given a target location that intends to initiate a promotion campaign, the goal is to _predict_ which users are influential with respect to the target location during the upcoming promotion period. Specifically, we aim to find which users in LBSN can generate more users to visit the target location when the promotion campaign starts. To the best of our knowledge, we are the first attempt to deal with the problem of TPD. TPD has some differences from location promotion. TPD aims to find the set of users as propagators who will actually generate more other users visiting the target POI _in the future_, but conventional location promotion is to find the set of users who have a large influence on the target POI using historical check-in data _in the past_. Users who are influential in historical check-in data might not imply they will still be influential in the future, especially on activating other users to visit the target POI. Since POI holders usually have limited funding, accurately finding future propagators can bring more revenue for the target POI. On the other hand, the TCD problem is to predict which users will visit the target location during the upcoming promotion period. With TCD, the promotion initiator of the given POI can perform targeted marketing to boost the possibility of a user visiting. In addition, with TCD, the POI holders can first understand the common traits of the potential customers in advance so that the marketing strategy can be well constructed. For the problem of TPD, it concerns the scenario that the POI holders, who aim to market or to promote sales, wonder which users are not only influential but also can attract more people to visit the POI in advance. Then POI holders can hire more influential people to market the POI that can increase the visited people to create more business opportunities. For example, a restaurant holder only has limited funds to employ three bloggers to advertise the restaurant. If we can find the potential candidates who have more attraction ability in the social network, by performing marketing based on these candidates, the POI will bring greater benefits to the restaurant holder. In other words, every venue would like to promote itself by recruiting some influential propagators under a limited budget.