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    Users Location Prediction in Location-based Social Networks

    Sixth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2015)

    31 August - 4 September 2015, Thessaloniki, Greece


    Jarana Manotumruksa



    The wealth of user-generated data in Location-Based Social Networks (LBSNs) has opened new opportunities for researchers to model and understand human mobile behaviour, including predicting where they are most likely to check-in next. In this paper, we propose a model that leverages the use of Global Temporal Preferences and Spatial Correlation, to help make predictions for a previously unseen user - the so-called cold-start problem. The experimental results on a real-world LBSN dataset show that our proposed model outperforms the state-of-the-art approaches on prediction accuracy and can alleviate the cold-start problem.


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