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      Users Location Prediction in Location-based Social Networks

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

      Future Directions in Information Access

      31 August - 4 September 2015

      Location-Based Social Networks, User’s Location Prediction, Cold-Start Problem

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          Abstract

          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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          Modeling temporal effects of human mobile behavior on location-based social networks

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            Author and article information

            Contributors
            Conference
            September 2015
            September 2015
            : 41-44
            Affiliations
            University of Glasgow
            Article
            10.14236/ewic/FDIA2015.11
            © Manotumruksa. Published by BCS Learning and Development Ltd. Proceedings of the 6 th Symposium on Future Directions in Information Access 2015

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Sixth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2015)
            FDIA 2015
            6
            Thessaloniki, Greece
            31 August - 4 September 2015
            Electronic Workshops in Computing (eWiC)
            Future Directions in Information Access

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