Phone call log as a context source to modeling individual user behavior

Iqbal H. Sarker; Alan Colman; Muhammad Ashad Kabir; Jun Han

doi:10.1145/2968219.2971592

Phone call log as a context source to modeling individual user behavior

Iqbal H. Sarker, Alan Colman, Muhammad Ashad Kabir, Jun Han

Swinburne University of Technology

Research output: Book chapter/Published conference paper › Conference paper › peer-review

28 Citations (Scopus)

4 Downloads (Pure)

Abstract

Mobile phone can record various types of context data related to a user's phone call activities in its call log. Call log provides temporal context to modeling individual user's phone call response behavior, i.e., when a user accepts, rejects or misses an incoming call. In this paper, we explore the potentiality of phone call log as a context source to modeling call response behavior of individual mobile users. Towards this, we present our initial work to generating temporal rules that capture the user's dominant call response behavior at various times of the day and days of the week, utilizing phone call log. Our preliminary experimental results on real datasets show that context information in call log can be used to model individual's phone call response behavior with high accuracy.

Original language	English
Title of host publication	Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing adjunct
Place of Publication	New York City, NY
Publisher	Association for Computing Machinery, Inc (ACM)
Pages	630-634
Number of pages	5
ISBN (Electronic)	9781450344623
DOIs	https://doi.org/10.1145/2968219.2971592
Publication status	Published - 12 Sept 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) - Kongresshaus Stadthalle Heidelberg, Heidelberg, Germany Duration: 12 Sept 2016 → 16 Sept 2016 https://web.archive.org/web/20160921113810/http://ubicomp.org/ubicomp2016/ (Conference website) https://web.archive.org/web/20161120020220/https://newfrontiersqs2.wordpress.com/program-2/ (Workshop program)

Conference

Conference	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)
Country/Territory	Germany
City	Heidelberg
Period	12/09/16 → 16/09/16
Other	The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) will be held in Heidelberg, Germany from Sep. 12-16, colocated with ISWC 2016. UbiComp 2016 will again be multi-track and we aim to include a broad multidisciplinary program, encompassing any work that one would previously expect to see at either the UbiComp or Pervasive conferences.
Internet address	https://web.archive.org/web/20160921113810/http://ubicomp.org/ubicomp2016/ (Conference website) https://web.archive.org/web/20161120020220/https://newfrontiersqs2.wordpress.com/program-2/ (Workshop program)

Access to Document

10.1145/2968219.2971592

Cite this

@inproceedings{8db46af19d4e463987771d4b29883490,

title = "Phone call log as a context source to modeling individual user behavior",

abstract = "Mobile phone can record various types of context data related to a user's phone call activities in its call log. Call log provides temporal context to modeling individual user's phone call response behavior, i.e., when a user accepts, rejects or misses an incoming call. In this paper, we explore the potentiality of phone call log as a context source to modeling call response behavior of individual mobile users. Towards this, we present our initial work to generating temporal rules that capture the user's dominant call response behavior at various times of the day and days of the week, utilizing phone call log. Our preliminary experimental results on real datasets show that context information in call log can be used to model individual's phone call response behavior with high accuracy.",

keywords = "Call Log, Mobile Phone, Rule Discovery, Temporal Context, User Behavior Modeling",

author = "Sarker, {Iqbal H.} and Alan Colman and Kabir, {Muhammad Ashad} and Jun Han",

note = "Includes bibliographical references.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) ; Conference date: 12-09-2016 Through 16-09-2016",

year = "2016",

month = sep,

day = "12",

doi = "10.1145/2968219.2971592",

language = "English",

pages = "630--634",

booktitle = "Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing adjunct",

publisher = "Association for Computing Machinery, Inc (ACM)",

url = "https://web.archive.org/web/20160921113810/http://ubicomp.org/ubicomp2016/, https://web.archive.org/web/20161120020220/https://newfrontiersqs2.wordpress.com/program-2/",

}

Sarker, IH, Colman, A, Kabir, MA & Han, J 2016, Phone call log as a context source to modeling individual user behavior. in Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing adjunct. Association for Computing Machinery, Inc (ACM), New York City, NY, pp. 630-634, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), Heidelberg, Baden-Württemberg, Germany, 12/09/16. https://doi.org/10.1145/2968219.2971592

Phone call log as a context source to modeling individual user behavior. / Sarker, Iqbal H.; Colman, Alan; Kabir, Muhammad Ashad et al.
Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing adjunct. New York City, NY: Association for Computing Machinery, Inc (ACM), 2016. p. 630-634.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Phone call log as a context source to modeling individual user behavior

AU - Sarker, Iqbal H.

AU - Colman, Alan

AU - Kabir, Muhammad Ashad

AU - Han, Jun

N1 - Includes bibliographical references.

PY - 2016/9/12

Y1 - 2016/9/12

N2 - Mobile phone can record various types of context data related to a user's phone call activities in its call log. Call log provides temporal context to modeling individual user's phone call response behavior, i.e., when a user accepts, rejects or misses an incoming call. In this paper, we explore the potentiality of phone call log as a context source to modeling call response behavior of individual mobile users. Towards this, we present our initial work to generating temporal rules that capture the user's dominant call response behavior at various times of the day and days of the week, utilizing phone call log. Our preliminary experimental results on real datasets show that context information in call log can be used to model individual's phone call response behavior with high accuracy.

AB - Mobile phone can record various types of context data related to a user's phone call activities in its call log. Call log provides temporal context to modeling individual user's phone call response behavior, i.e., when a user accepts, rejects or misses an incoming call. In this paper, we explore the potentiality of phone call log as a context source to modeling call response behavior of individual mobile users. Towards this, we present our initial work to generating temporal rules that capture the user's dominant call response behavior at various times of the day and days of the week, utilizing phone call log. Our preliminary experimental results on real datasets show that context information in call log can be used to model individual's phone call response behavior with high accuracy.

KW - Call Log

KW - Mobile Phone

KW - Rule Discovery

KW - Temporal Context

KW - User Behavior Modeling

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BT - Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing adjunct

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ER -

Phone call log as a context source to modeling individual user behavior

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Transforming Phone Call Behavioral Modeling into Innovations in Automated Action Systems for Electronic Devices

Cite this

Phone call log as a context source to modeling individual user behavior

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Impacts

Transforming Phone Call Behavioral Modeling into Innovations in Automated Action Systems for Electronic Devices

Cite this