Predicting how you respond to phone calls: Towards discovering temporal behavioral rules

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

Research output: Book chapter/Published conference paperConference paper

1 Citation (Scopus)
8 Downloads (Pure)

Abstract

Discovering temporal rules that capture an individual's phone call response behavior is essential to building intelligent individualized call interruption management system. The key challenge to discovering such temporal rules is identifying within a phone call log the time boundaries that delineate periods when an individual user rejects or accepts phone calls. Moreover, potential data sparsity in phone call logs imposes additional challenge in discovering applicable rules. In this paper, we address the above issues and present a hybrid approach to identify the effective time boundaries for discovering temporal behavioral rules of individual mobile phone users utilizing calendar and mobile phone data. Our preliminary experiments on real datasets show that our proposed hybrid approach dynamically identifies better time boundaries based on like behavioral patterns and outperforms the existing calendar-based approach (CBA) and log-based approach (LBA) to discovering the temporal behavior rules of individual mobile phone users.
Original languageEnglish
Title of host publicationProceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016
Place of PublicationNew York City, New York, United States
PublisherAssociation for Computing Machinery, Inc (ACM)
Pages421-425
Number of pages5
ISBN (Electronic)9781450346184
DOIs
Publication statusPublished - 29 Nov 2016
Event28th Australian Conference on Human-Computer Interaction (HCI): OzCHI 2016 - Grand Chancellor Hotel, Launceston, Australia
Duration: 29 Nov 201602 Dec 2016
http://www.ozchi.org/ozchi2016/

Conference

Conference28th Australian Conference on Human-Computer Interaction (HCI)
CountryAustralia
CityLaunceston
Period29/11/1602/12/16
OtherOzCHI is the annual non-profit conference for the Computer-Human Interaction Special Interest Group (CHISIG) and Australia's leading forum for the latest in HCI research and practice. OzCHI attracts a broad international community of researchers, industry practitioners, academics and students. Participants come from a range of backgrounds, including interface designers, user experience (UX) practitioners, information architects, software engineers, human factors experts, information systems analysts and social scientists. The conference theme is Connected Futures, highlighting the importance of connectedness through technology.
Internet address

Fingerprint

Mobile phones
Intelligent buildings
Experiments

Cite this

Sarker, I. H., Kabir, M. A., Colman, A., & Han, J. (2016). Predicting how you respond to phone calls: Towards discovering temporal behavioral rules. In Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016 (pp. 421-425). New York City, New York, United States: Association for Computing Machinery, Inc (ACM). https://doi.org/10.1145/3010915.3010979
Sarker, Iqbal H. ; Kabir, Muhammad Ashad ; Colman, Alan ; Han, Jun. / Predicting how you respond to phone calls : Towards discovering temporal behavioral rules. Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016. New York City, New York, United States : Association for Computing Machinery, Inc (ACM), 2016. pp. 421-425
@inproceedings{b6bdca20b81d43b3b9562e6a7bf523e9,
title = "Predicting how you respond to phone calls: Towards discovering temporal behavioral rules",
abstract = "Discovering temporal rules that capture an individual's phone call response behavior is essential to building intelligent individualized call interruption management system. The key challenge to discovering such temporal rules is identifying within a phone call log the time boundaries that delineate periods when an individual user rejects or accepts phone calls. Moreover, potential data sparsity in phone call logs imposes additional challenge in discovering applicable rules. In this paper, we address the above issues and present a hybrid approach to identify the effective time boundaries for discovering temporal behavioral rules of individual mobile phone users utilizing calendar and mobile phone data. Our preliminary experiments on real datasets show that our proposed hybrid approach dynamically identifies better time boundaries based on like behavioral patterns and outperforms the existing calendar-based approach (CBA) and log-based approach (LBA) to discovering the temporal behavior rules of individual mobile phone users.",
keywords = "Calendar, Call interruptions, Call response behavior, Data sparsity, Mobile data mining, Personalization, Phone call logs, Temporal rules",
author = "Sarker, {Iqbal H.} and Kabir, {Muhammad Ashad} and Alan Colman and Jun Han",
year = "2016",
month = "11",
day = "29",
doi = "10.1145/3010915.3010979",
language = "English",
pages = "421--425",
booktitle = "Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016",
publisher = "Association for Computing Machinery, Inc (ACM)",

}

Sarker, IH, Kabir, MA, Colman, A & Han, J 2016, Predicting how you respond to phone calls: Towards discovering temporal behavioral rules. in Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016. Association for Computing Machinery, Inc (ACM), New York City, New York, United States, pp. 421-425, 28th Australian Conference on Human-Computer Interaction (HCI), Launceston, Australia, 29/11/16. https://doi.org/10.1145/3010915.3010979

Predicting how you respond to phone calls : Towards discovering temporal behavioral rules. / Sarker, Iqbal H.; Kabir, Muhammad Ashad; Colman, Alan; Han, Jun.

Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016. New York City, New York, United States : Association for Computing Machinery, Inc (ACM), 2016. p. 421-425.

Research output: Book chapter/Published conference paperConference paper

TY - GEN

T1 - Predicting how you respond to phone calls

T2 - Towards discovering temporal behavioral rules

AU - Sarker, Iqbal H.

AU - Kabir, Muhammad Ashad

AU - Colman, Alan

AU - Han, Jun

PY - 2016/11/29

Y1 - 2016/11/29

N2 - Discovering temporal rules that capture an individual's phone call response behavior is essential to building intelligent individualized call interruption management system. The key challenge to discovering such temporal rules is identifying within a phone call log the time boundaries that delineate periods when an individual user rejects or accepts phone calls. Moreover, potential data sparsity in phone call logs imposes additional challenge in discovering applicable rules. In this paper, we address the above issues and present a hybrid approach to identify the effective time boundaries for discovering temporal behavioral rules of individual mobile phone users utilizing calendar and mobile phone data. Our preliminary experiments on real datasets show that our proposed hybrid approach dynamically identifies better time boundaries based on like behavioral patterns and outperforms the existing calendar-based approach (CBA) and log-based approach (LBA) to discovering the temporal behavior rules of individual mobile phone users.

AB - Discovering temporal rules that capture an individual's phone call response behavior is essential to building intelligent individualized call interruption management system. The key challenge to discovering such temporal rules is identifying within a phone call log the time boundaries that delineate periods when an individual user rejects or accepts phone calls. Moreover, potential data sparsity in phone call logs imposes additional challenge in discovering applicable rules. In this paper, we address the above issues and present a hybrid approach to identify the effective time boundaries for discovering temporal behavioral rules of individual mobile phone users utilizing calendar and mobile phone data. Our preliminary experiments on real datasets show that our proposed hybrid approach dynamically identifies better time boundaries based on like behavioral patterns and outperforms the existing calendar-based approach (CBA) and log-based approach (LBA) to discovering the temporal behavior rules of individual mobile phone users.

KW - Calendar

KW - Call interruptions

KW - Call response behavior

KW - Data sparsity

KW - Mobile data mining

KW - Personalization

KW - Phone call logs

KW - Temporal rules

UR - http://www.scopus.com/inward/record.url?scp=85012031814&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85012031814&partnerID=8YFLogxK

U2 - 10.1145/3010915.3010979

DO - 10.1145/3010915.3010979

M3 - Conference paper

AN - SCOPUS:85012031814

SP - 421

EP - 425

BT - Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016

PB - Association for Computing Machinery, Inc (ACM)

CY - New York City, New York, United States

ER -

Sarker IH, Kabir MA, Colman A, Han J. Predicting how you respond to phone calls: Towards discovering temporal behavioral rules. In Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016. New York City, New York, United States: Association for Computing Machinery, Inc (ACM). 2016. p. 421-425 https://doi.org/10.1145/3010915.3010979