Estimating online user location distribution without GPS location

Yusheng Xie, Yu Cheng, Ankit Agrawal, Alok Nidhi Choudhary

Research output: Contribution to journalConference articlepeer-review

Abstract

We focus on the problem of offline user location estimation using online information, particularly for the application of TV segment advertising. Unlike previous works, the proposed method does not assume GPS information, but works with loosely structured information such as English location description. We propose to use a neural language model to capture the semantic similarity among the location descriptions. The language model can help reduce the otherwise expensive geolocating service lookups by internally resolving similar areas, neighborhoods, etc. Onto the same description. We also propose a metric for comparing geodemographic histograms. This metric considers the demographic gap between the online world and the offline world. In the experiments section, we demonstrate the recall and accuracy of our language-based, GPS-free user location distribution estimation. In addition, we illustrate the effectiveness of the proposed distribution estimation metric.

Original languageEnglish (US)
Article number7022697
Pages (from-to)936-943
Number of pages8
JournalIEEE International Conference on Data Mining Workshops, ICDMW
Volume2015-January
Issue numberJanuary
DOIs
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: Dec 14 2014 → …

Keywords

  • GPS
  • location
  • offline social network
  • online social network

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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