Understanding recency-based behavior model for individual mobile phone users

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

doi:10.1145/3123024.3124570

Understanding recency-based behavior model for individual mobile phone users

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

Swinburne University of Technology

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

4 Citations (Scopus)

Abstract

Mobile phone log data is not static as it is progressively added to day-by-day according to individual’s behavior. The goal of this position paper is to highlight the issues of traditional behavior modeling utilizing phone log data and to describe the key aspects that constitute the foundation of our recency-based behavior modeling for individual mobile phone users to overcome such issues.

Original language	English
Title of host publication	UbiComp/ISWC '17 adjunct
Subtitle of host publication	Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers
Place of Publication	United States
Publisher	Association for Computing Machinery, Inc (ACM)
Pages	916-921
Number of pages	6
ISBN (Print)	9781450351904
DOIs	https://doi.org/10.1145/3123024.3124570
Publication status	Published - 2017
Event	ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'17) - Grand Wailea, Wailea, Maui, United States Duration: 11 Sept 2017 → 15 Sept 2017 https://iswc.net/iswc17/ (Conference website (ISWC)) http://www.ubicomp.org/ubicomp2017/index.html (Conference website (Ubicomp)) https://dl.acm.org/citation.cfm?id=3123024&picked=prox (Conference proceedings)

Conference

Conference	ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'17)
Country/Territory	United States
City	Wailea, Maui
Period	11/09/17 → 15/09/17
Other	The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UBICOMP 2017) is the premier outlet for novel research contributions in ubiquitous and pervasive computing systems. It is the result of a merger of the two most renowned conferences in the field: Pervasive and UBICOMP. The 2017 International Symposium on Wearable Computers (ISWC 2017) is the premier forum for wearable computing and issues related to on-body and worn mobile technologies. It brings together researchers, product vendors, fashion designers, textile manufacturers, users, and related professionals. UBICOMP 2017 and ISWC 2017 will be held as a single event, featuring a single registration rate. Attendees of either conference will have full access to all tracks of the other. Register here to attend either conference.
Internet address	https://iswc.net/iswc17/ (Conference website (ISWC)) http://www.ubicomp.org/ubicomp2017/index.html (Conference website (Ubicomp)) https://dl.acm.org/citation.cfm?id=3123024&picked=prox (Conference proceedings)

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10.1145/3123024.3124570

Cite this

Sarker, I. H., Kabir, A., Colman, A., & Han, J. (2017). Understanding recency-based behavior model for individual mobile phone users. In UbiComp/ISWC '17 adjunct: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers (pp. 916-921). Association for Computing Machinery, Inc (ACM). https://doi.org/10.1145/3123024.3124570

Sarker, Iqbal H. ; Kabir, Ashad ; Colman, Alan et al. / Understanding recency-based behavior model for individual mobile phone users. UbiComp/ISWC '17 adjunct: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers. United States : Association for Computing Machinery, Inc (ACM), 2017. pp. 916-921

@inproceedings{30c83925022a4b959b7a67890304369e,

title = "Understanding recency-based behavior model for individual mobile phone users",

abstract = "Mobile phone log data is not static as it is progressively added to day-by-day according to individual{\textquoteright}s behavior. The goal of this position paper is to highlight the issues of traditional behavior modeling utilizing phone log data and to describe the key aspects that constitute the foundation of our recency-based behavior modeling for individual mobile phone users to overcome such issues.",

keywords = "Mobile data mining, User behavior modeling, Incremental rule mining, Recency",

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

note = "Includes bibliographical references.; ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'17) ; Conference date: 11-09-2017 Through 15-09-2017",

year = "2017",

doi = "10.1145/3123024.3124570",

language = "English",

isbn = "9781450351904",

pages = "916--921",

booktitle = "UbiComp/ISWC '17 adjunct",

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

url = "https://iswc.net/iswc17/, http://www.ubicomp.org/ubicomp2017/index.html, https://dl.acm.org/citation.cfm?id=3123024&picked=prox",

}

Sarker, IH, Kabir, A, Colman, A & Han, J 2017, Understanding recency-based behavior model for individual mobile phone users. in UbiComp/ISWC '17 adjunct: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers. Association for Computing Machinery, Inc (ACM), United States, pp. 916-921, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'17) , Wailea, Maui, Hawaii, United States, 11/09/17. https://doi.org/10.1145/3123024.3124570

Understanding recency-based behavior model for individual mobile phone users. / Sarker, Iqbal H.; Kabir, Ashad; Colman, Alan et al.
UbiComp/ISWC '17 adjunct: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers. United States: Association for Computing Machinery, Inc (ACM), 2017. p. 916-921.

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

TY - GEN

T1 - Understanding recency-based behavior model for individual mobile phone users

AU - Sarker, Iqbal H.

AU - Kabir, Ashad

AU - Colman, Alan

AU - Han, Jun

N1 - Includes bibliographical references.

PY - 2017

Y1 - 2017

N2 - Mobile phone log data is not static as it is progressively added to day-by-day according to individual’s behavior. The goal of this position paper is to highlight the issues of traditional behavior modeling utilizing phone log data and to describe the key aspects that constitute the foundation of our recency-based behavior modeling for individual mobile phone users to overcome such issues.

AB - Mobile phone log data is not static as it is progressively added to day-by-day according to individual’s behavior. The goal of this position paper is to highlight the issues of traditional behavior modeling utilizing phone log data and to describe the key aspects that constitute the foundation of our recency-based behavior modeling for individual mobile phone users to overcome such issues.

KW - Mobile data mining

KW - User behavior modeling

KW - Incremental rule mining

KW - Recency

U2 - 10.1145/3123024.3124570

DO - 10.1145/3123024.3124570

M3 - Conference paper

SN - 9781450351904

SP - 916

EP - 921

BT - UbiComp/ISWC '17 adjunct

PB - Association for Computing Machinery, Inc (ACM)

CY - United States

T2 - ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'17)

Y2 - 11 September 2017 through 15 September 2017

ER -

Sarker IH, Kabir A, Colman A, Han J. Understanding recency-based behavior model for individual mobile phone users. In UbiComp/ISWC '17 adjunct: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers. United States: Association for Computing Machinery, Inc (ACM). 2017. p. 916-921 doi: 10.1145/3123024.3124570

Understanding recency-based behavior model for individual mobile phone users

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