Place Recommendation Using Location-Based Services and Real-time Social Network Data

Currently, there is excessively growing information
about places on Facebook, which is the largest social network but
such information is not explicitly organized and ranked. Therefore
users cannot exploit such data to recommend places conveniently and
quickly. This paper proposes a Facebook application and an Android
application that recommend places based on the number of check-ins
of those places, the distance of those places from the current location,
the number of people who like Facebook page of those places, and
the number of talking about of those places. Related Facebook data is
gathered via Facebook API requests. The experimental results of the
developed applications show that the applications can recommend
places and rank interesting places from the most to the least. We have
found that the average satisfied score of the proposed Facebook
application is 4.8 out of 5. The users’ satisfaction can increase by
adding the app features that support personalization in terms of
interests and preferences.





References:
[1] Gartner. “Gartner Says Annual Smartphone Sales Surpassed Sales of
Feature Phones for the First Time in 2013” at http://www.gartner.com/
newsroom/id/2665715
[2] GooglePlay Store “Social Places Recommendations Based onLocation”
at https://play.google.com/store/search?q=Social%20Places%
20Recommendations%20%20Based%20on%20Location&c=apps&hl=t
h
[3] D. Lun Lee, W. Chien Lee, K. Wai-Ting Leung. “CLR: A Collaborative
Location Recommendation Framework based on Co-Clustering.” in 11
Proceedings of the 34th international, July, 2011, Beijing,
China, p. 24-28.
[4] M. Balduini, I. Celino, D. Dell’Aglio, Y. Huangc E. D. Valle, Y. Huang,
T. Lee, S. Ho Kim, and V. Tresp “BOTTARI: An augmented reality
mobile application to deliver personalized and location-based
recommendations by continuous analysis of social media streams”, in
Web Semantics: Science, Services and Agents on the World Wide Web,
November, 2012 , p. 33-41.
[5] C. Zhiming, C. Hongbo, M.S. Arefin, and Y. Morimoto. “Place
Recommendation from Check-in Spots on Location-Based Online Social
Networks.”, in Networking and Computing (ICNC), 2012 Third
International Conference, December, 2012, Okinawa Japan ,p. 143 –
148.
[6] Oracle. “Using REST Web Services”. at http://docs.oracle.com/cd/
E24152_01/Platform.101/ATGWSFrameGuide/html/s1301usingrestweb
services01.html
[7] M. Reto.Creating “Applications and Activities”. Android
PROFESSIONAL2 , Application Development. Indianapolis , Wiley
Publishing , 2010.
[8] Geo-Informatics Center for Thailand, “Meaning of Geographic
Information System”, at http://www.gisthai.org/about-gis/gis.html
[9] K. Runapongsa Saikaew. “Social Media” at http://www.slideshare.net
/krunapon/social-media-5661152
[10] Techcrunch. “Facebook Passes 1B Mobile Users, 200M Messenger
Users In Q1.” at http://techcrunch.com/2014/04/23/ facebook-passes-1bmobile-
monthly-active-users-in-q1-as-mobile-ads-reach-59-of-all-adsales/?
ncid=rss
[11] S.A. Hossain, A.S.M.M. Rahman, T.T. Tran, and A.E. Saddik.
“Location Aware Question Answering based Product Searching in
Mobile Handheld Devices.” in Distributed Simulation and Real Time
Applications (DS-RT), 2010 IEEE/ACM 14th International Symposium,
Oct, 2010, p. 189 - 195.
[12] Z. Du, R. Chen, and Xianhua Shu. “Research on mobile Location
Service Design Based on Android.” in Wireless Communications,
Networking and Mobile Computing, 2009. WiCom '09. 5th International
Conference, Sept, 2009 , Beijing , p. 1-4.
[13] Zocialinc. “Behavior of Thai Social Network.” at
http://blog.zocialinc.com/thailand-socialnetwork-connection/