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Geographic Segmentation via Latent Poisson Factor Model

Authors:

Andrew Gelfand,

Yan LiuAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 357 - 366

https://doi.org/10.1145/2835776.2835806

Published: 08 February 2016 Publication History

Abstract

Discovering latent structures in spatial data is of critical importance to understanding the user behavior of location-based services. In this paper, we study the problem of geographic segmentation of spatial data, which involves dividing a collection of observations into distinct geo-spatial regions and uncovering abstract correlation structures in the data. We introduce a novel, Latent Poisson Factor (LPF) model to describe spatial count data. The model describes the spatial counts as a Poisson distribution with a mean that factors over a joint item-location latent space. The latent factors are constrained with weak labels to help uncover interesting spatial dependencies. We study the LPF model on a mobile app usage data set and a news article readership data set. We empirically demonstrate its effectiveness on a variety of prediction tasks on these two data sets.

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Cited By

Lu HNiu WCaverlee JCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User FactorizationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210044(205-214)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210044
Kurashima TAlthoff TLeskovec JChampin PGandon FMédini LLalmas MIpeirotis P(2018)Modeling Interdependent and Periodic Real-World Action SequencesProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186161(803-812)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186161
Zhao KYuan QCong G(2018)Spatiotemporal Topic Detection from Social MediaEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110175(2948-2956)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110175
Show More Cited By

Index Terms

Geographic Segmentation via Latent Poisson Factor Model
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 February 2016

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NSF IIS
U. S. Army Research Office
USC Integrated Media System Center (IMSC)

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WSDM 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Lu HNiu WCaverlee JCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User FactorizationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210044(205-214)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210044
Kurashima TAlthoff TLeskovec JChampin PGandon FMédini LLalmas MIpeirotis P(2018)Modeling Interdependent and Periodic Real-World Action SequencesProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186161(803-812)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186161
Zhao KYuan QCong G(2018)Spatiotemporal Topic Detection from Social MediaEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110175(2948-2956)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110175
Zhao KYuan QCong G(2017)Spatiotemporal Topic Detection from Social MediaEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110175-1(1-9)Online publication date: 9-Aug-2017
https://doi.org/10.1007/978-1-4614-7163-9_110175-1

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