poster

Using flickr geotags to predict user travel behaviour

Authors:

Maarten Clements,

Pavel Serdyukov,

Arjen P. de Vries,

Marcel J.T. ReindersAuthors Info & Claims

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 851 - 852

https://doi.org/10.1145/1835449.1835648

Published: 19 July 2010 Publication History

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Abstract

We propose a method to predict a user's favourite locations in a city, based on his Flickr geotags in other cities. We define a similarity between the geotag distributions of two users based on a Gaussian kernel convolution. The geotags of the most similar users are then combined to rerank the popular locations in the target city personalised for this user.

We show that this method can give personalised travel recommendations for users with a clear preference for a specific type of landmark.

References

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T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. In SIRIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 103--110, New York, NY, USA, 2007. ACM Press.

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Y. Takeuchi and M. Sugimoto. Cityvoyager: An outdoor recommendation system based on user location history. In J. Ma, H. Jin, L. T. Yang, and J. J. P. Tsai, editors, Ubiquitous Intelligence and Computing, volume 4159, chapter 64, pages 625--636. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006.

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V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. Collaborative location and activity recommendations with gps history data. In WWW '10: Proceeding of the 19th international conference on World Wide Web, page 10, New York, NY, USA, April 2010. ACM.

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Cited By

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Chen LLyu DYu SChen G(2023)Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged PhotosACM Transactions on Knowledge Discovery from Data10.1145/358201517:7(1-18)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3582015
Renjith SSreekumar AJathavedan M(2022)An extensive study on the evolution of context-aware personalized travel recommender systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2019.10207857:1Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2019.102078
Deseure-Charron FDjebali SGuérard G(2022)Clustering Method for Touristic Photographic Spots RecommendationAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_17(223-237)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-22137-8_17
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Index Terms

Using flickr geotags to predict user travel behaviour
1. Information systems
  1. Information retrieval

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Published In

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

July 2010

944 pages

ISBN:9781450301534

DOI:10.1145/1835449

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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SIGIR '10

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SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

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SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Chen LLyu DYu SChen G(2023)Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged PhotosACM Transactions on Knowledge Discovery from Data10.1145/358201517:7(1-18)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3582015
Renjith SSreekumar AJathavedan M(2022)An extensive study on the evolution of context-aware personalized travel recommender systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2019.10207857:1Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2019.102078
Deseure-Charron FDjebali SGuérard G(2022)Clustering Method for Touristic Photographic Spots RecommendationAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_17(223-237)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-22137-8_17
Chen YHuang HChiu SLee C(2021)Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social NetworksISPRS International Journal of Geo-Information10.3390/ijgi1002005710:2(57)Online publication date: 30-Jan-2021
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Lyu DChen LXu ZYu S(2020)Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photosApplied Intelligence10.1007/s10489-019-01566-650:3(924-938)Online publication date: 1-Mar-2020
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Giglio SBertacchini FBilotta EPantano P(2019)Machine learning and points of interest: typical tourist Italian citiesCurrent Issues in Tourism10.1080/13683500.2019.163782723:13(1646-1658)Online publication date: 12-Jul-2019
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