Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors

Location-Based Social Networks (LBSN) such as Foursquare allow users to indicate venue visits via check-ins. This results in much fine grained context-rich data, useful for studying user mobility. In this work, we use check-ins to characterize trips and visitors to two cities, where visitors are def...

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Main Authors: CHONG, Wen Haw, DAI, Bingtian, 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/3104
https://ink.library.smu.edu.sg/context/sis_research/article/4104/viewcontent/Not_All_Trips_Equal_pv_oa.pdf
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spelling sg-smu-ink.sis_research-41042020-03-30T08:55:28Z Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors CHONG, Wen Haw DAI, Bingtian LIM, Ee Peng Location-Based Social Networks (LBSN) such as Foursquare allow users to indicate venue visits via check-ins. This results in much fine grained context-rich data, useful for studying user mobility. In this work, we use check-ins to characterize trips and visitors to two cities, where visitors are defined as having their home cities elsewhere. First, we divide trips into two duration types: long and short. We then show that trip types differ in check-in distributions over venue categories, time slots, as well as check-in intensity. Based on the trip types, we then divide visitors into long-term and short-term visitors. We compare visitor types in terms of popularities of check-in venues and proximities to friends' check-ins. Our findings indicate that short-term visitors are more biased towards popular venues. As for proximity to friends' check-ins, the effect is more consistently observed for long-term visitors. These findings also illustrate that locations of incoming visitors can effectively be analyzed using LBSN data in addition to conducting user surveys which are relatively costlier. Lastly, we investigate the importance of visitor type information in models for venue prediction. We apply models including a state of the art kernel density estimation technique and ranking based on venue popularity. For each model, we consider two settings where visitor type information is absent/present. For long-term visitors, we observed little differences in accuracies. However, for short-term visitors, predictions are significantly more accurate by using type information. These findings suggest that venue prediction or recommender systems should consider visitor type to improve accuracy. 2015-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3104 info:doi/10.1145/2817946.2817958 https://ink.library.smu.edu.sg/context/sis_research/article/4104/viewcontent/Not_All_Trips_Equal_pv_oa.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 Check-in Foursquare Long-term Visitors Short-term Computer Sciences Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Check-in
Foursquare
Long-term
Visitors
Short-term
Computer Sciences
Databases and Information Systems
spellingShingle Check-in
Foursquare
Long-term
Visitors
Short-term
Computer Sciences
Databases and Information Systems
CHONG, Wen Haw
DAI, Bingtian
LIM, Ee Peng
Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
description Location-Based Social Networks (LBSN) such as Foursquare allow users to indicate venue visits via check-ins. This results in much fine grained context-rich data, useful for studying user mobility. In this work, we use check-ins to characterize trips and visitors to two cities, where visitors are defined as having their home cities elsewhere. First, we divide trips into two duration types: long and short. We then show that trip types differ in check-in distributions over venue categories, time slots, as well as check-in intensity. Based on the trip types, we then divide visitors into long-term and short-term visitors. We compare visitor types in terms of popularities of check-in venues and proximities to friends' check-ins. Our findings indicate that short-term visitors are more biased towards popular venues. As for proximity to friends' check-ins, the effect is more consistently observed for long-term visitors. These findings also illustrate that locations of incoming visitors can effectively be analyzed using LBSN data in addition to conducting user surveys which are relatively costlier. Lastly, we investigate the importance of visitor type information in models for venue prediction. We apply models including a state of the art kernel density estimation technique and ranking based on venue popularity. For each model, we consider two settings where visitor type information is absent/present. For long-term visitors, we observed little differences in accuracies. However, for short-term visitors, predictions are significantly more accurate by using type information. These findings suggest that venue prediction or recommender systems should consider visitor type to improve accuracy.
format text
author CHONG, Wen Haw
DAI, Bingtian
LIM, Ee Peng
author_facet CHONG, Wen Haw
DAI, Bingtian
LIM, Ee Peng
author_sort CHONG, Wen Haw
title Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
title_short Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
title_full Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
title_fullStr Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
title_full_unstemmed Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
title_sort not all trips are equal: analyzing foursquare check-ins of trips and city visitors
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/3104
https://ink.library.smu.edu.sg/context/sis_research/article/4104/viewcontent/Not_All_Trips_Equal_pv_oa.pdf
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