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Trajectory Segmentation for a Recommendation Module of a Customer Relationship Management System
Blekinge Inst Technol, Master Programe Comp Sci, Karlskrona, Sweden..
Telenor, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)In: Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 / [ed] Wu, Y Min, G Georgalas, N AlDubi, A Jin, X Yang, L Ma, J Yang, P, IEEE , 2017, p. 1150-1155Conference paper, Published paper (Refereed)
Abstract [en]

In business analytics some industries rely heavily on commercial geo-demographic segmentation systems (MOSAIC, ACORN, etc.), which are a universally strong predictor of user's behavior: from diabetes propensity and purchasing habits to political preferences. A segment is defined with a postcode of the client's home address. Recent research suggests that a mature competitor to geo-demographic segmentation is about to emerge: segmentation based on user mobility is reported to be a reliable proxy of social well-being of the neighborhood. In this submission, we have completed a user segmentation model based on clustering of user trajectories from the Call Detail Records covering one week of activity of one region in Sweden. The new segmentation has been compared against MOSAIC in the recommendation module of a customer relationship management system and has revealed better business options with regard to network exploitation and potential revenues. The implementation is available from the corresponding author (JS or LL) on request.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1150-1155
Keywords [en]
trajectory clustering, user segmentation, spectral clustering
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16057DOI: 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.177ISI: 000426972400177OAI: oai:DiVA.org:bth-16057DiVA, id: diva2:1195055
Conference
EEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-07-10Bibliographically approved

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Lundberg, LarsSidorova, Yulia

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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