Long-term user location prediction using deep learning and periodic pattern mining

Mun Hou Wong, S. Tseng*, Jerry C.C. Tseng, Sun Wei Liu, Cheng Hung Tsai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In recent years, with the advances in mobile communication and growing popularity of the fourth-generation mobile network along with the enhancement in location positioning techniques, mobile devices have generated extensive spatial trajectory data, which represent the mobility of moving objects. New services are emerged to serve mobile users based on their predicted locations. Most of the existing studies on location prediction were focused on predicting the next location of a user, which is regarded as short-term next location prediction. While more advanced location-based services could be enabled for the users if long-term location prediction could be achieved, the existing methods constrained in next-location prediction are not applicable for long-term prediction scenario. In this paper, we propose a novel prediction framework named LSTM-PPM that utilises deep learning and periodic pattern mining for long-term prediction of user locations. Our framework devises the ideology from natural language model and uses multi-step recursive strategy to perform long-term prediction. Furthermore, the periodic pattern mining technique is utilized to reduce the accumulated loss in the multi-step strategy. Through empirical evaluation on a real-life trajectory dataset, our proposed approach is shown to provide effective performance in long-term location prediction. To the best of our knowledge, this is the first work addressing the research topic on long-term user location prediction.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 13th International Conference, ADMA 2017, Proceedings
EditorsWen-Chih Peng, Wei Emma Zhang, Gao Cong, Aixin Sun, Chengliang Li
PublisherSpringer Verlag
Pages582-594
Number of pages13
ISBN (Print)9783319691787
DOIs
StatePublished - 1 Jan 2017
Event13th International Conference on Advanced Data Mining and Applications, ADMA 2017 - Singapore, Singapore
Duration: 5 Nov 20176 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10604 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Advanced Data Mining and Applications, ADMA 2017
CountrySingapore
CitySingapore
Period5/11/176/11/17

Keywords

  • Location prediction
  • Long-Term prediction
  • Trajectory pattern mining

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