Prediction of user location using the radiation model and social check-ins

Location-based social networks serve as a source of data for a wide range of applications, from recommendation of places to visit to modelling of city traffic, and urban planning. One of the basic problems in all these areas is the formulation of a predictive model for the location of a certain user at a certain time. In this paper, we propose a new approach for predicting user location, which uses two components to make the prediction, based on (i) coordinates and times of user check-ins and (ii) social interaction between different users. We improve the performance of a state-of-the art model using the radiation model of spatial choice and a social component based on the frequency of matching check-ins of user's friends. Friendship is defined by the presence of reciprocal following on Twitter. Our empirical results highlight an improvement over the state-of-the-art in terms of accuracy, and suggest practical solutions for spatio-temporal and socially-inspired prediction of user location.