Location Prediction: A Temporal-Spatial Bayesian Model


In social networks, predicting a user&#8217;s location mainly depends on those of his/her friends, where the key lies in how to select his/her most influential friends. In this article, we analyze the theoretically maximal accuracy of location prediction based on friends&#8217; locations and compare it with the practical accuracy obtained by the state-of-the-art location prediction methods. Upon observing a big gap between the theoretical and practical accuracy, we propose a new strategy for selecting influential friends in order to improve the practical location prediction accuracy. Specifically, several features are defined to measure the influence of the friends on a user&#8217;s location, based on which we put forth a sequential random-walk-with-restart procedure to rank the friends of the user in terms of their influence. By dynamically selecting the top <i>N</i> most influential friends of the user per time slice, we develop a temporal-spatial Bayesian model to characterize the dynamics of friends&#8217; influence for location prediction. Finally, extensive experimental results on datasets of real social networks demonstrate that the proposed influential friend selection method and temporal-spatial Bayesian model can significantly improve the accuracy of location prediction.

DOI: 10.1145/2816824

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@article{Jia2016LocationPA, title={Location Prediction: A Temporal-Spatial Bayesian Model}, author={Yantao Jia and Yuanzhuo Wang and Xiaolong Jin and Xueqi Cheng}, journal={ACM TIST}, year={2016}, volume={7}, pages={31:1-31:25} }