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Prediction of user app usage behavior from geo-spatial data
Beijing University of Posts and Telecommunications.
Beijing University of Posts and Telecommunications.
Beijing University of Posts and Telecommunications.
Beijing University of Posts and Telecommunications.
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Number of Authors: 52016 (English)In: GeoRich '16: Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, New York: ACM Digital Library, 2016, p. 37-42, article id 7Conference paper, Published paper (Refereed)
Abstract [en]

In the era of mobile Internet, a vast amount of geo-spatial data allows us to gain further insights into human activities, which is critical for Internet Services Providers (ISP) to provide better personalized services. With the pervasiveness of mobile Internet, much evidence show that human mobility has heavy impact on app usage behavior. In this paper, we propose a method based on machine learning to predict users' app usage behavior using several features of human mobility extracted from geo-spatial data in mobile Internet traces. The core idea of our method is selecting a set of mobility attributes (e.g. location, travel pattern, and mobility indicators) that have large impact on app usage behavior and inputting them into a classification model. We evaluate our method using real-world network traffic collected by our self-developed high-speed Traffic Monitoring System (TMS). Our prediction method achieves 90.3% accuracy in our experiment, which verifies the strong correlation between human mobility and app usage behavior. Our experimental results uncover a big potential of geo-spatial data extracted from mobile Internet

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2016. p. 37-42, article id 7
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-35372DOI: 10.1145/2948649.2948656ISI: 000390470000007Scopus ID: 2-s2.0-84979222024Local ID: 9e347669-2b43-4318-8d54-b4f147bd512aISBN: 978-1-4503-4309-1 (print)OAI: oai:DiVA.org:ltu-35372DiVA, id: diva2:1008625
Conference
International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data : 26/06/2016 - 26/06/2016
Note

Godkänd; 2016; 20160627 (andbra)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved

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Lindgren, Anders

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • nn-NB
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More languages
Output format
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