###### Abstract
Therefore, it is crucial to devise a prediction-based method that can accurately identify such propagators for effective location promotion. Moreover, for the problem of TCD, it concerns that the POI holders, who aim to advertise their POIs, want to perceive who the potential customers are when designing the promotion plan. The POI holders can analyze the potential customers to know the characteristics they have, and develop an effective marketing strategy that can successfully attract such potential customers. For example, if we can find the potential users who have a larger chance of visiting the restaurant, then the restaurant can send some discount coupons or send some mails to advertise the POI so that their probabilities of visiting the POI can be remarkably boosted. That said, not all users will be interested in the targeted POI, and our goal is to develop a prediction-based method to find those potential customers. By doing so, the POI holders can avoid putting resources on irrelevant users, and concentrate more on potential customers. We take advantage of social relations and user-POI interactions in LBSN to make predictions in both TPD and TCD problems. While existing studies estimate the number of activated users via information propagation [11; 16; 17], we think such simulation-based influence estimation cannot reflect the potential of a user becoming influential, i.e., affecting other users to visit the target POI. Therefore, we propose a network embedding method, LBSN2 vec, to depict users and their interactions with POIs. The idea is to jointly embed users in the social network as well as their check-in records at POIs into a low-dimensional space. The embedding space is expected to preserve user preferences as well as user-user interactions in visiting POIs. In the embedding space, two users close to each other can reflect that: (a) they have similar preferences of POI visiting, and (b) they have higher potential to affect one another. That is, one of them visiting a new POI tends to give higher motivation for the other also to visit that POI. In addition, since check-in records are also used in learning embeddings, the embedding vectors of users and POIs are comparable. And the distance between a user and a POI can imply user preference in visiting that POI. A closer distance between a user and a POI can reflect that the user has a higher desire to visit the POI. Consequently, for a given POI, we propose some similarity measures to retrieve influential users as propagators and potential users as customers. Here we would like to elaborate the underlying reasons that we adopt the embedding learning approach to solve the proposed tasks. To better motivate the proposed method, we create Table 1 to systematically compare the neural network embedding-based approach with the simple statistical scoring heuristics in different aspects. First, both our TPD and TCD tasks aim to predict the future behaviors of users with respect to the given targeted POI. Existing node scoring measurements, such as the weighted neighborhood size and the number of involved geodesics, mainly rely on a variety of statistical calculations, less regarding predictability. The proposed embedding learning methods can better model how users interact with POIs, and thus properly work for predicting future propagators and customers. Second, the statistical methods, such as the number of geodesics ending in targeted POI, multiplying the neighborhood size or centrality score, cannot model the semantic behaviors of users. That said, although two users could have totally different visited POIs, these POIs can belong to similar types. If we do not model the semantic behaviors of users via exploiting their high-order interactions in the check-in graph, the user influence and user's predictability to the target POI cannot be well learned. The proposed embedding learning method is to better depict the high-order semantic relationships between users and items in the vector space. This is the main reason that we adopt the embedding learning method.