Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other's physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a joint model called LoCaTe, consisting of (i) user mobility model estimated using kernel density estimates; (ii) a model of the semantics of the location using topic models; and (iii) a model of time-gap between checkins using exponential distribution. We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 -Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that LoCaTe significantly outperforms state-of-the-art models for the same task.

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Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called "check-in" profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns.In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Shortest path querying is a fundamental graph problem which is computationally quite challenging when operating over massive scale graphs. Recent results have addressed the problem of computing either exact or good approximate shortest path distances efficiently. Some of these techniques also return the path corresponding to the estimated shortest path distance fast.However, none of these techniques work very well when we have additional constraints on the labels associated with edges that constitute the path. In this paper, we develop SkIt index structure, which supports a wide range of label constraints on paths, and returns an accurate estimation of the shortest path that satisfies the constraints. We conduct experiments over graphs such as social networks, and knowledge graphs that contain millions of nodes/edges, and show that SkIt index is fast, accurate in the estimated distance and has a high recall for paths that satisfy the constraints.

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other's physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a framework called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a user correlation model that uses an exponential distribution. We further develop LoCaTe+, an advanced model within the same framework where user correlation is quantified using a Mutually Exciting Hawkes Process. We show the applicability of LoCaTe and LoCaTe+ for location promotion and location recommendation tasks using LBSNs. Our models are validated using a long-term crawl of Foursquare data collected between Jan 2015-Feb 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of the LoCaTe framework in capturing location-specific influence between users. We also show that our models improve over state-of-the-art models for the task of location promotion as well as location recommendation.