Behavior-oriented time segmentation for mining individualized rules of mobile phone users

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

Research output: Book chapter/Published conference paperConference paperpeer-review

16 Citations (Scopus)

Abstract

Mobile or cellular phones can record various types of context data related to a user's phone call activities. In this paper, we present an approach to discovering individualized behavior rules for mobile users from their phone call records, based on the temporal context in which a user accepts, rejects or misses a call. One of the determinants of an individual's phone behavior is the various activities undertaken at various times of a day and days of the week. In many cases, such behavior will follow temporal patterns. Currently, researchers modeling user behavior using temporal context statically segment time into arbitrary categories (e.g., morning, evening) or periods (e.g., 1 hour). However, such time categorization does not necessarily map to the patterns of individual user activity and subsequent behavior. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that dynamically identifies diverse time segments for an individual user's behaviors based on the phone call records. Experiments on real datasets show that our proposed technique better captures the user's dominant call response behavior at various times of the day and week, thereby enabling more appropriate rules to be created for the purpose of automated handling of incoming calls, in an intelligent call interruption management system.
Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE international conference on data science and advanced analytics
Subtitle of host publicationDSAA 2016
Place of PublicationUnited States
PublisherIEEE
Pages488-497
Number of pages10
ISBN (Electronic)9781509052066
ISBN (Print)9781509052073 (Print on demand)
DOIs
Publication statusPublished - 2016
EventIEEE DSAA 2016: 3rd IEEE International Conference on Data Science and Advanced Analytics - Montreal Marriott Chateau Champlain, Montréal, Canada
Duration: 17 Oct 201619 Oct 2016
https://sites.ualberta.ca/~dsaa16/ (Conference website)
https://sites.ualberta.ca/~dsaa16/docs/IEEE-DSAA2016-announce2.pdf (Conference brochure)

Conference

ConferenceIEEE DSAA 2016
Country/TerritoryCanada
CityMontréal
Period17/10/1619/10/16
OtherFollowing the first successful edition DSAA'2014 held in 2014 in Shanghai, then the second successful edition DSAA'2015 held in Paris, the 2016 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2016), sponsored by the IEEE Computational Intelligence Society, aims to provide a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications.

IEEE DSAA'2016 will consist of two main Tracks: Research and Application; the Research Track is aimed at collecting contributions related to theoretical foundations of Data Science and Data Analytics. The Application Track is aimed at collecting contributions related to applications of Data Science and Data Analytics in real life scenarios. DSAA solicits then both theoretical and practical works on data science and advanced analytics.
Internet address

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