Check-in Location Prediction Using Wavelets and Conditional Random Fields

The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse problems, which have prompted the prediction of future check-ins to be very challenging. In this paper, we propose a novel enhanced location predictor for check-in data that is crafted using Poisson distribution, Wavelets and Conditional Random Fields (CRF). Specifically, we show that check-in generation is governed by the Poisson distribution. In addition, among others, we utilize wavelets to rigorously analyze social influence and learn elusive underlying patterns, as well as human mobility behaviors embedded in check-in data. We utilize this knowledge to institute CRF features, which capture latent trends that govern users' mobility. These CRF features are employed to build a robust predictive model that predicts future locations with enhanced accuracy. We demonstrate the effectiveness of our predictive model on two real datasets. Furthermore, our experiments reveal that our approach outperforms a state-of-the-art work with an accuracy of 36%.