short-paper

Mining social media to create personalized recommendations for tourist visits

Authors:

Adrian Popescu,

Gregory GrefenstetteAuthors Info & Claims

COM.Geo '11: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications

Article No.: 37, Pages 1 - 6

https://doi.org/10.1145/1999320.1999357

Published: 23 May 2011 Publication History

Abstract

Photo sharing platforms users often annotate their trip photos with landmark names. These annotations can be aggregated in order to recommend lists of popular visitor attractions similar to those found in classical tourist guides. However, individual tourist preferences can vary significantly so good recommendations should be tailored to individual tastes. Here we pose this visit personalization as a collaborative filtering problem. We mine the record of visited landmarks exposed in online user data to build a user-user similarity matrix. When a user wants to visit a new destination, a list of potentially interesting visitor attractions is produced based on the experience of like-minded users who already visited that destination. We compare our recommender to a baseline which simulates classical tourist guides on a large sample of Flickr users.

References

[1]

M. Ames, M. Naaman. Why we tag: motivations for annotation in mobile and online media. In Proc. of SIGCHI 2007 (San Jose, California, USA), p. 971--980.

Digital Library

[2]

A. Beeharee et al. Exploiting real world knowledge in ubiquitous applications. Personal and Ubiquitous Computing 11(6), 429--437.

Digital Library

[3]

L. Cantoni, S. Tardini, A. Inversini, E. Marchiori. From Paradigmatic to Syntagmatic Communities: A Socio-Semiotic Approach to the Evolution Pattern of Online Travel Communities. In Proc. of Information and Communication Technologies in Tourism 2009.

[4]

D. Crandall, L. Backstrom, D. Huttenlocher, J. Kleinberg. Mapping the World's Photos. In Proc. of WWW 2009.

Digital Library

[5]

DBPedia -- http:://dbpedia.org

[6]

J. Delgado, N. Ishii. Memory-Based Weighted Majority Prediction. ACM SIGIR'99 Workshop on Recommender Systems (Berkeley, CA, USA).

[7]

F. Girardin, F. Dal Fiore, J. Blat, C. Ratti. Understanding of Tourist Dynamics from Explicitly Disclosed Location Information. In Proc. of the 4th International Symposium on LBS and Telecartography (Hong-Kong, China, 2007).

[8]

D. Goldberg, D. Nichols, B. M. Oki, D. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, v. 35 n. 12, p. 61--70, Dec. 1992

Digital Library

[9]

F. Gonotti, et al. Trajectory pattern mining. In Proc. of SIGKDD 2007, pp. 330--339.

Digital Library

[10]

R. Ji, X. Xie, H. Yao, W. Y. Ma. Mining City Landmarks by Graph Modeling. In Proc. of ACM Multimedia 2009.

Digital Library

[11]

L. Kennedy, M. Naaman. Generating diverse and representative image search results for landmarks. In Proc. of WWW 2008.

Digital Library

[12]

N. Mamoulis et al. Mining, Indexing and Querying Historical Spatiotemporal Data. In Proc. of SIGKDD 2004, p. 236--245.

Digital Library

[13]

B. Mehta, T. Hofmann, W. Nejdl. Robust Collaborative Filtering. Proc of ACM Recommender Systems 2007.

Digital Library

[14]

A. Popescu, G. Grefenstette, P.-A. Moëllic. Gazetiki: automatic construction of a geographical gazetteer. In Proc. of JCDL 2008 (Pittsburgh, PA, June 2008).

Digital Library

[15]

A. Popescu, G. Grefenstette, P.-A. Moëllic. Mining Tourist Information from User-Supplied Collections. In Proc. of CIKM 2009 (Hong Kong, RPC, November 2009).

Digital Library

[16]

T. Quack, B. Leibe, L. van Gool. World-Scale Mining of Objects and Events from Community Photo Collections, In Proc. of ACM CIVR 2008 (Niagara Falls, Canada).

Digital Library

[17]

T. Rattenbury, N. Good, M. Naaman. Towards Automatic Extraction of Event and Place Semantics from Flickr Tags. In Proc. of SIGIR 2007.

Digital Library

[18]

F. Ricci. Travel Recommender Systems. IEEE Intelligent Systems, Nov. -- Dec. 2002.

[19]

TripAdvisor - http://tripadvisor.com

Cited By

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
Soumm MPopescu ADelezoide B(2023)Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00300(2986-2996)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00300
Yamagishi KOcampo LAbellana DTanaid RTiu AMedalla MSelerio EGo COlorvida RMaupo AMaskariño DTantoo E(2021)The impact of social media marketing strategies on promoting sustainability of tourism with fuzzy cognitive mapping: a case of Kalanggaman Island (Philippines)Environment, Development and Sustainability10.1007/s10668-021-01283-623:10(14998-15030)Online publication date: 12-Mar-2021
https://doi.org/10.1007/s10668-021-01283-6
Show More Cited By

Index Terms

Mining social media to create personalized recommendations for tourist visits
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
  2. World Wide Web
    1. Web applications
      1. Internet communications tools

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cover image ACM Other conferences

COM.Geo '11: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications

May 2011

292 pages

ISBN:9781450306812

DOI:10.1145/1999320

Conference Chair:
Lindi Liao

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2011

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Agence Nationale de la Recherche

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COM.Geo '11

COM.Geo '11: 2nd International Conference on Computing for Geospatial Research & Application

May 23 - 25, 2011

DC, Washington, USA

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Cited By

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
Soumm MPopescu ADelezoide B(2023)Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00300(2986-2996)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00300
Yamagishi KOcampo LAbellana DTanaid RTiu AMedalla MSelerio EGo COlorvida RMaupo AMaskariño DTantoo E(2021)The impact of social media marketing strategies on promoting sustainability of tourism with fuzzy cognitive mapping: a case of Kalanggaman Island (Philippines)Environment, Development and Sustainability10.1007/s10668-021-01283-623:10(14998-15030)Online publication date: 12-Mar-2021
https://doi.org/10.1007/s10668-021-01283-6
Hidaka MKanaya YKawanaka SMatsuda YNakamura YSuwa HFujimoto MArakawa YYasumoto K(2020)On-site Trip Planning Support System Based on Dynamic Information on Tourism SpotsSmart Cities10.3390/smartcities30200133:2(212-231)Online publication date: 10-Apr-2020
https://doi.org/10.3390/smartcities3020013
Cuenca-Jara JTerroso-Sáenz FValdés-Vela MSkarmeta A(2020)Classification of spatio-temporal trajectories from Volunteer Geographic Information through fuzzy rulesApplied Soft Computing10.1016/j.asoc.2019.10591686:COnline publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.asoc.2019.105916
Binski NNatapov ADalyot S(2019)Retrieving Landmark Salience Based on Wikipedia: An Integrated Ranking ModelISPRS International Journal of Geo-Information10.3390/ijgi81205298:12(529)Online publication date: 26-Nov-2019
https://doi.org/10.3390/ijgi8120529
Lyu DChen LXu ZYu S(2019)Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photosApplied Intelligence10.1007/s10489-019-01566-6Online publication date: 24-Oct-2019
https://doi.org/10.1007/s10489-019-01566-6
Tao YBoth ADuckham M(2017)Analytics of movement through checkpointsInternational Journal of Geographical Information Science10.1080/13658816.2017.139767532:7(1282-1303)Online publication date: 9-Nov-2017
https://doi.org/10.1080/13658816.2017.1397675
Bujari ACiman MGaggi OPalazzi C(2017)Using gamification to discover cultural heritage locations from geo-tagged photosPersonal and Ubiquitous Computing10.1007/s00779-016-0989-621:2(235-252)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s00779-016-0989-6
Lee JTsou M(2017)Mapping Spatiotemporal Tourist Behaviors and Hotspots Through Location-Based Photo-Sharing Service (Flickr) DataProgress in Location Based Services 201810.1007/978-3-319-71470-7_16(315-334)Online publication date: 9-Dec-2017
https://doi.org/10.1007/978-3-319-71470-7_16
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