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Understanding User Behavior Geospatially

Authors: Xing Xie

Published in: Dagstuhl Seminar Proceedings, Volume 8251, Contextual and Social Media Understanding and Usage (2009)

Abstract

Understanding users is an essential task for providing personal Web experience and targeted advertisements. Current commercial or research systems try to understand users from their online behaviors, for example, how they search, read and write on the Web. However, this type of approaches missed a large part of people¡¯s everyday life, or called ¡®physical¡¯ behaviors. The physical behaviors include how people dine, shop, travel, or other activities happened in the real world. In our opinion, location is one of the most important aspects for people¡¯s everyday life. With the rapid growth of location sensing devices and Web based GIS tools, it becomes possible to track these physical behaviors from a geospatial view. In this paper, we present our recent work towards understanding users from a geospatial view. Particularly, we studied GPS trajectory transportation mode categorization and co-located query pattern mining problems.

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Xing Xie. Understanding User Behavior Geospatially. In Contextual and Social Media Understanding and Usage. Dagstuhl Seminar Proceedings, Volume 8251, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{xie:DagSemProc.08251.7,
  author =	{Xie, Xing},
  title =	{{Understanding User Behavior Geospatially}},
  booktitle =	{Contextual and Social Media Understanding and Usage},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8251},
  editor =	{Susanne Boll and Mohan S. Kankanhalli and Gopal Pingali and Svetha Venkatesh},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08251.7},
  URN =		{urn:nbn:de:0030-drops-20177},
  doi =		{10.4230/DagSemProc.08251.7},
  annote =	{Keywords: Geographic data mining, personalization, transportation mode, co-location pattern, log mining}
}

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Understanding User Behavior Geospatially

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