Prediction of venues in foursquare using flipped topic models

Foursquare is a highly popular location-based social platform, where users indicate their presence at venues via check-ins and/or provide venue-related tips. On Foursquare, we explore Latent Dirichlet Allocation (LDA) topic models for venue prediction: predict venues that a user is likely to visit,...

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Main Authors: CHONG, Wen Haw, DAI, Bing Tian, LIM, Ee Peng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/2625
https://ink.library.smu.edu.sg/context/sis_research/article/3625/viewcontent/130___Prediction_of_Venues_in_Foursquare_Using_Flipped_Topic_Models__ECIR2015_.pdf
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spelling sg-smu-ink.sis_research-36252018-07-13T04:26:08Z Prediction of venues in foursquare using flipped topic models CHONG, Wen Haw DAI, Bing Tian LIM, Ee Peng Foursquare is a highly popular location-based social platform, where users indicate their presence at venues via check-ins and/or provide venue-related tips. On Foursquare, we explore Latent Dirichlet Allocation (LDA) topic models for venue prediction: predict venues that a user is likely to visit, given his history of other visited venues. However we depart from prior works which regard the users as documents and their visited venues as terms. Instead we ‘flip’ LDA models such that we regard venues as documents that attract users, which are now the terms. Flipping is simple and requires no changes to the LDA mechanism. Yet it improves prediction accuracy significantly as shown in our experiments. Furthermore, flipped models are superior when we model tips and check-ins as separate modes. This enables us to use tips to improve prediction accuracy, which is previously unexplored. Lastly, we observed the largest accuracy improvement for venues with fewer visitors, implying that the flipped models cope with sparse venue data more effectively. 2015-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2625 info:doi/10.1007/978-3-319-16354-3_69 https://ink.library.smu.edu.sg/context/sis_research/article/3625/viewcontent/130___Prediction_of_Venues_in_Foursquare_Using_Flipped_Topic_Models__ECIR2015_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Foursquare Venue prediction Topic models Databases and Information Systems Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Foursquare
Venue prediction
Topic models
Databases and Information Systems
Social Media
spellingShingle Foursquare
Venue prediction
Topic models
Databases and Information Systems
Social Media
CHONG, Wen Haw
DAI, Bing Tian
LIM, Ee Peng
Prediction of venues in foursquare using flipped topic models
description Foursquare is a highly popular location-based social platform, where users indicate their presence at venues via check-ins and/or provide venue-related tips. On Foursquare, we explore Latent Dirichlet Allocation (LDA) topic models for venue prediction: predict venues that a user is likely to visit, given his history of other visited venues. However we depart from prior works which regard the users as documents and their visited venues as terms. Instead we ‘flip’ LDA models such that we regard venues as documents that attract users, which are now the terms. Flipping is simple and requires no changes to the LDA mechanism. Yet it improves prediction accuracy significantly as shown in our experiments. Furthermore, flipped models are superior when we model tips and check-ins as separate modes. This enables us to use tips to improve prediction accuracy, which is previously unexplored. Lastly, we observed the largest accuracy improvement for venues with fewer visitors, implying that the flipped models cope with sparse venue data more effectively.
format text
author CHONG, Wen Haw
DAI, Bing Tian
LIM, Ee Peng
author_facet CHONG, Wen Haw
DAI, Bing Tian
LIM, Ee Peng
author_sort CHONG, Wen Haw
title Prediction of venues in foursquare using flipped topic models
title_short Prediction of venues in foursquare using flipped topic models
title_full Prediction of venues in foursquare using flipped topic models
title_fullStr Prediction of venues in foursquare using flipped topic models
title_full_unstemmed Prediction of venues in foursquare using flipped topic models
title_sort prediction of venues in foursquare using flipped topic models
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/2625
https://ink.library.smu.edu.sg/context/sis_research/article/3625/viewcontent/130___Prediction_of_Venues_in_Foursquare_Using_Flipped_Topic_Models__ECIR2015_.pdf
_version_ 1770572528291414016