Analyzing Location Predictability on Location-Based Social Networks

With the growing popularity of location-based social networks, vast amount of user check-in histories have been accumulated. Based on such historical data, predicting a user’s next check-in place is of much interest recently. There is, however, little study on the limit of predictability of this task and its correlation with users’ demographics. These studies can give deeper insight to the prediction task and bring valuable insights to the design of new prediction algorithms. In this paper, we carry out a thorough study on the limit of check-in location predictability, i.e., to what extent the next locations are predictable, in the presence of special properties of check-in traces. Specifically, we begin with estimating the entropy of an individual check-in trace and then leverage Fano’s inequality to transform it to predictability. Extensive analysis has then been performed on two large-scale check-in datasets from Jiepang and Gowalla with 36M and 6M check-ins, respectively. As a result, we find 25% and 38% potential predictability respectively. Finally, the correlation analysis between predictability and users’ demographics has been performed. The results show that the demographics, such as gender and age, are significantly correlated with location predictability.