Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns

Eyal Ben Zion; Boaz Lerner

Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns

Eyal Ben Zion, Boaz Lerner

Department of Industrial Engineering and Management

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Many applications benefit from learning human behaviors and lifestyle. Different trajectories can represent a behavior, and previous behaviors and trajectories can inuence decisions on further behaviors and on visiting future places and taking familiar or new trajectories. To more accurately explain and predict personal behavior, we extend a topic model to capture temporal relations among previous trajectories/weeks and cur- rent ones. In addition, we show how different trajectories may have the same latent cause, which we relate to lifestyle. The code for our algorithm is available online.

Original language	American English
Title of host publication	ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages	459-464
Number of pages	6
ISBN (Electronic)	9782875870391
State	Published - 1 Jan 2017
Event	25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 - Bruges, Belgium Duration: 26 Apr 2017 → 28 Apr 2017

Conference

Conference	25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017
Country/Territory	Belgium
City	Bruges
Period	26/04/17 → 28/04/17

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems

Cite this

@inproceedings{4af51d0f07f24cec8afe4639bda85c98,

title = "Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns",

abstract = "Many applications benefit from learning human behaviors and lifestyle. Different trajectories can represent a behavior, and previous behaviors and trajectories can inuence decisions on further behaviors and on visiting future places and taking familiar or new trajectories. To more accurately explain and predict personal behavior, we extend a topic model to capture temporal relations among previous trajectories/weeks and cur- rent ones. In addition, we show how different trajectories may have the same latent cause, which we relate to lifestyle. The code for our algorithm is available online.",

author = "Zion, {Eyal Ben} and Boaz Lerner",

note = "Publisher Copyright: {\textcopyright} ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.; 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 ; Conference date: 26-04-2017 Through 28-04-2017",

year = "2017",

month = jan,

day = "1",

language = "American English",

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booktitle = "ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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Zion, EB & Lerner, B 2017, Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns. in ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 459-464, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017, Bruges, Belgium, 26/04/17.

Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns. / Zion, Eyal Ben; Lerner, Boaz.
ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2017. p. 459-464.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns

AU - Zion, Eyal Ben

AU - Lerner, Boaz

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Many applications benefit from learning human behaviors and lifestyle. Different trajectories can represent a behavior, and previous behaviors and trajectories can inuence decisions on further behaviors and on visiting future places and taking familiar or new trajectories. To more accurately explain and predict personal behavior, we extend a topic model to capture temporal relations among previous trajectories/weeks and cur- rent ones. In addition, we show how different trajectories may have the same latent cause, which we relate to lifestyle. The code for our algorithm is available online.

AB - Many applications benefit from learning human behaviors and lifestyle. Different trajectories can represent a behavior, and previous behaviors and trajectories can inuence decisions on further behaviors and on visiting future places and taking familiar or new trajectories. To more accurately explain and predict personal behavior, we extend a topic model to capture temporal relations among previous trajectories/weeks and cur- rent ones. In addition, we show how different trajectories may have the same latent cause, which we relate to lifestyle. The code for our algorithm is available online.

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M3 - Conference contribution

SP - 459

EP - 464

BT - ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

T2 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

Y2 - 26 April 2017 through 28 April 2017

ER -

Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns

Abstract

Conference

All Science Journal Classification (ASJC) codes

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