demonstration

Recommending interesting events in real-time with foursquare check-ins

Authors:

Max Sklar,

Blake Shaw,

Andrew HogueAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 311 - 312

https://doi.org/10.1145/2365952.2366028

Published: 09 September 2012 Publication History

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Abstract

Foursquare is a location-based social application that helps users explore the world around them and share their experiences with friends. When foursquare users visit places, they ``check in" using their mobile phones, indicating they are at that place. People check in for a variety of reasons: to keep up with friends, get tips about places, redeem rewards, and keep track of their personal history. In aggregate, billions of these check-ins reveal distinct patterns about when places are popular and allow us to build a unique place recommendation engine which can identify and recommend interesting events in real-time based on statistical deviations from past historical trends.

References

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Shao WChen XZhao JXia LZhang JYin D(2023)Sequential Recommendation with User Evolving Preference DecompositionProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625312(253-263)Online publication date: 26-Nov-2023
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Zhang YZong RKou ZShang LWang D(2022)CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/35551796:CSCW2(1-29)Online publication date: 11-Nov-2022
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Wilken RHumphreys L(2019)Constructing the check-in: Reflections on photo-taking among Foursquare usersCommunication and the Public10.1177/20570473198533284:2(100-117)Online publication date: 12-Jun-2019
https://doi.org/10.1177/2057047319853328
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Information

Published In

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 09 September 2012

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SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Shao WChen XZhao JXia LZhang JYin D(2023)Sequential Recommendation with User Evolving Preference DecompositionProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625312(253-263)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625312
Zhang YZong RKou ZShang LWang D(2022)CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/35551796:CSCW2(1-29)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555179
Wilken RHumphreys L(2019)Constructing the check-in: Reflections on photo-taking among Foursquare usersCommunication and the Public10.1177/20570473198533284:2(100-117)Online publication date: 12-Jun-2019
https://doi.org/10.1177/2057047319853328
Silva TViana ABenevenuto FVillas LSalles JLoureiro AQuercia D(2019)Urban Computing Leveraging Location-Based Social Network DataACM Computing Surveys10.1145/330128452:1(1-39)Online publication date: 13-Feb-2019
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Krueger RHan QIvanov NMahtal SThom DPfister HErtl T(2019)Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location DataComputer Graphics Forum10.1111/cgf.1371338:3(595-607)Online publication date: 10-Jul-2019
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Liu SWang BXu M(2018)SERGE: Successive Event Recommendation Based on Graph Entropy for Event-Based Social NetworksIEEE Access10.1109/ACCESS.2017.27866796(3020-3030)Online publication date: 2018
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Exploring trajectory-driven local geographic topics in foursquare

Performing a check-in: emerging practices, norms and 'conflicts' in location-sharing using foursquare

A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior

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