Abstract
Mobile service providers and manufacturers continue to provide services and devices that take advantage of the location information associated with devices to provide a more personalized experience for users. For many such services, the user experience can be dramatically improved if a mobile device can predict how long a mobile user will stay at the current location. In this paper, we propose to take advantage of contextual information for predicting the stay time of mobile users. Specially, we investigate two strategies for modeling the relevance between it and contextual information, i.e., Stay Status Prediction (SSP) and Stay Time Prediction (STP). SSP is to predict whether a mobile user will stay at the current location at time point according to the contextual information at, while STP is to directly predict how long a mobile user will stay at the current location. Moreover, we study several typical machine learning models which can be extended for implementing SSP and STP and evaluate their performance with respect to prediction accuracy. We also conduct extensive experiments on real data sets to evaluate several implementations of the proposed strategies in terms of both effectiveness and efficiency for STP.