e-journal

Predicting Stay Time of Mobile Users With Contextual Information

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Mobile service providers andmanufacturers continue to provide services and devices that take advantage of the location information associated with devices to provide a more personalized experience for users. For many such services, the user experience can be dramatically improved if a mobile device can predict how long a mobile user will stay at the current location. In this paper, we propose to take advantage of contextual information for predicting the stay time of mobile users. Specially, we investigate two strategies for modeling the relevance between it and contextual information, i.e., Stay Status Prediction (SSP) and Stay Time Prediction (STP). SSP is to predict whether a mobile user
will stay at the current location at time point according to the contextual information at , while STP is to directly predict how long a mobile user will stay at the current location.Moreover, we study several typical machine learning models which can be extended for implementing SSP and STP and evaluate their performance with respect to prediction accuracy.We also conduct extensive experiments on real data sets to evaluate several implementations of the proposed strategies in terms of both effectiveness and
efficiency for STP. Note to Practitioners—Automatically and accurately predicting the stay time of mobile users at a location is very important in enabling service providers to offer their customers with the desired services. This work for the first time develops two novel prediction strategies by using historical contextual information and the resulting STP system by using machine learning algorithms. Such a system can be easily implemented in a smart phone that possesses the GPS and other sensors. This work also examines such issues as prediction performance, memory requirements, and energy consumption
of the developed system. The research results show that it can be readily deployed for practical applications.
Index Terms—Contextual information, mobile users, stay time prediction (STP).

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Ketersediaan

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2013

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 10, NO. 4, OCTOBER 2013

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 10, NO. 4

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

yuli/agus

Versi lain/terkait

Lampiran Berkas

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