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Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics"

Published: 05 September 2012 Publication History

Abstract

In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent "themes". Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).

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cover image ACM Conferences
UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing
September 2012
1268 pages
ISBN:9781450312240
DOI:10.1145/2370216
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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Published: 05 September 2012

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Author Tags

  1. foursquare
  2. location-based service
  3. topic modeling

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  • Research-article

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Ubicomp '12
Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing
September 5 - 8, 2012
Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;
Overall Acceptance Rate 764 of 2,912 submissions, 26%

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  • (2022)Towards multi-dimensional knowledge-aware approach for effective community detection in LBSNWorld Wide Web10.1007/s11280-022-01101-726:4(1435-1458)Online publication date: 14-Sep-2022
  • (2021)Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNsIEEE Access10.1109/ACCESS.2020.30476809(3140-3154)Online publication date: 2021
  • (2021)A multi‐agent system for itinerary suggestion in smart environmentsCAAI Transactions on Intelligence Technology10.1049/cit2.120566:4(377-393)Online publication date: 8-Sep-2021
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