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Using flickr geotags to predict user travel behaviour

Published: 19 July 2010 Publication History

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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D. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world's photos. In WWW '09: Proceeding of the 18th international conference on World Wide Web, pages 761--770, 2009.
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K. Järvelin and J. Kekalainen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4):422--446, October 2002.
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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.
[4]
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.

Cited By

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  • (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
  • (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
  • (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
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Published In

cover image ACM Conferences
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
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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Author Tags

  1. flickr
  2. geotag
  3. recommendation

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SIGIR '10
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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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Cited By

View all
  • (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
  • (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
  • (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
  • (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
  • (2021)Extracting Relevant Social Geo-Tagged Photos for Points of InterestResearch in Intelligent and Computing in Engineering10.1007/978-981-15-7527-3_70(747-756)Online publication date: 5-Jan-2021
  • (2021)Proximity Based Social Networking in Urban Environments: Applications, Architectures and FrameworksPrecision Positioning with Commercial Smartphones in Urban Environments10.1007/978-3-030-71288-4_4(71-107)Online publication date: 6-Aug-2021
  • (2020)Mining sequential activity–travel patterns for individual‐level human activity prediction using Bayesian networksTransactions in GIS10.1111/tgis.1263524:5(1341-1358)Online publication date: 30-May-2020
  • (2020)Survey on context‐aware tour guide systemsIET Smart Cities10.1049/iet-smc.2019.00102:1(34-42)Online publication date: 24-Feb-2020
  • (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
  • (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
  • Show More Cited By

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