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HandWiki. Location-Based Recommendation. Encyclopedia. Available online: https://encyclopedia.pub/entry/36966 (accessed on 02 October 2026).
HandWiki. Location-Based Recommendation. Encyclopedia. Available at: https://encyclopedia.pub/entry/36966. Accessed October 02, 2026.
HandWiki. "Location-Based Recommendation" Encyclopedia, https://encyclopedia.pub/entry/36966 (accessed October 02, 2026).
HandWiki. (2022, November 29). Location-Based Recommendation. In Encyclopedia. https://encyclopedia.pub/entry/36966
HandWiki. "Location-Based Recommendation." Encyclopedia. Web. 29 November, 2022.
Location-Based Recommendation
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Location-based recommendation is a recommender system that incorporates location information, such as that from a mobile device, into algorithms to attempt to provide more-relevant recommendations to users. This could include recommendations for restaurants, museums, or other points of interest or events near the user's location. These services take advantage of the increasing use of smartphones that store and provide the location information of their users alongside location-based social networks (LBSN), like Foursquare, Gowalla, Swarm, and Yelp. In addition to geosocial networking services, traditional online social networks such as Facebook and Twitter are using the location information of their users to show and recommend upcoming events, posts, and local trends. In addition to its value for users, this information is valuable for third-party companies to advertise products, hotels, places, and to forecast service demand such as the number of taxis needed in a part of a city.

online social networks social networks recommender system

References

  1. Ye, Mao; Yin, Peifeng; Lee, Wang-Chien; Lee, Dik-Lun (2011-01-01). Exploiting Geographical Influence for Collaborative Point-of-interest Recommendation. SIGIR '11. New York, NY, USA: ACM. 325–334. doi:10.1145/2009916.2009962. ISBN 9781450307574.  https://dx.doi.org/10.1145%2F2009916.2009962
  2. Noulas, Anastasios; Scellato, Salvatore; Mascolo, Cecilia; Pontil, Massimiliano (2011-01-01). An empirical study of geographic user activity patterns in foursquare. pp. 570–573. http://www.citeulike.org/user/amirreza/article/9770331. 
  3. Brockmann, D.; Hufnagel, L.; Geisel, T. (2006-01-26). "The scaling laws of human travel". Nature 439 (7075): 462–465. doi:10.1038/nature04292. ISSN 1476-4687. PMID 16437114. Bibcode: 2006Natur.439..462B.  https://dx.doi.org/10.1038%2Fnature04292
  4. Quercia, D.; Lathia, N.; Calabrese, F.; Lorenzo, G. Di; Crowcroft, J. (2010-12-01). Recommending Social Events from Mobile Phone Location Data. 971–976. doi:10.1109/ICDM.2010.152. ISBN 978-1-4244-9131-5.  https://dx.doi.org/10.1109%2FICDM.2010.152
  5. Zhang, A. X.; Noulas, A.; Scellato, S.; Mascolo, C. (2013-09-01). Hoodsquare: Modeling and Recommending Neighborhoods in Location-Based Social Networks. 69–74. doi:10.1109/SocialCom.2013.17. ISBN 978-0-7695-5137-1.  https://dx.doi.org/10.1109%2FSocialCom.2013.17
  6. Cranshaw, Justin; Schwartz, Raz; Hong, Jason I.; Sadeh, Norman. The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. ICWSM'12. 
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