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Lock_Hidden Location Prediction using Check-in Patterns in LBSN.pdf (808.27 kB)

Hidden location prediction using check-in patterns in location based social networks

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journal contribution
posted on 2017-11-22, 14:34 authored by Pramit Mazumdar, Korra Sathya Babu, Bidyut Patra, Russell LockRussell Lock
Check-in facility in a Location Based Social Network (LBSN) enables people to share location information as well as real life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN. Research into extrapolating unchecked locations from historical data is limited. Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user trajectory. The proposed ALPM explores the association between a user's checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state of the art work in literature.

History

School

  • Science

Department

  • Computer Science

Published in

Knowledge and Information Systems

Volume

57

Pages

571 - 601

Citation

MAZUMDAR, P. ...et al., 2017. Hidden location prediction using check-in patterns in location based social networks. Knowledge and Information Systems, 57 (3), pp.571–601.

Publisher

© Springer Verlag

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2018-01-30

Publication date

2018-02-15

Notes

This is a post-peer-review, pre-copyedit version of an article published in Knowledge and Information Systems. The final authenticated version is available online at: https://doi.org/10.1007/s10115-018-1170-5

ISSN

0219-1377

Language

  • en