Users Location Prediction in Location-based Social Networks

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.

Content

Author and article information

Contributors

Jarana Manotumruksa

Conference

Publication date: September 2015

Publication date (Print): September 2015

Pages: 41-44

Affiliations

University of Glasgow

Article

DOI: 10.14236/ewic/FDIA2015.11

SO-VID: a646a221-8bfa-4113-9018-119e96cfc7bb

Copyright statement: © Manotumruksa. Published by BCS Learning and Development Ltd. Proceedings of the 6 ^th Symposium on Future Directions in Information Access 2015

License:

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/

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

Conference acronym: FDIA 2015

Conference number: 6

Conference location: Thessaloniki, Greece

Conference date: 31 August - 4 September 2015

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Future Directions in Information Access

Product

Product Information: 1477-9358BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/FDIA2015.11

Self URI (journal page): https://ewic.bcs.org/

Users Location Prediction in Location-based Social Networks

Abstract