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Location Recommendation System based on LBSNS

LBSNS 기반 장소 추천 시스템

  • Jung, Ku-Imm (Department of Strategy Marketing, SeeOn Inc.) ;
  • Ahn, Byung-Ik (Department of Strategy Marketing, SeeOn Inc.) ;
  • Kim, Jeong-Joon (Division of Computer Science & Engineering, Konkuk University) ;
  • Han, Ki-Joon (Division of Computer Science & Engineering, Konkuk University)
  • 정구임 ((주)씨온 전략마케팅팀) ;
  • 안병익 ((주)씨온 전략마케팅팀) ;
  • 김정준 (건국대학교 컴퓨터공학부) ;
  • 한기준 (건국대학교 컴퓨터공학부)
  • Received : 2014.04.04
  • Accepted : 2014.06.20
  • Published : 2014.06.28

Abstract

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

Keywords

LBSNS;Check-in Data Analysis;Location Recommendation;User Similarity

Acknowledgement

Supported by : 중소기업청

References

  1. H. Y. Kim and J. H. Oh, The Current state and social meaning of domestic and foreign SNS. Hongik University, 2012.
  2. J. M. Ryu, C. P. Hong, K. B. Kang, D. H. Kang, D. Y. Yangand J. W. Jwa, Development of mobile context awareness restaurant recommendation services. The Korea Contents Association, Vol. 7, No. 5, pp. 138-145, 2007. https://doi.org/10.5392/JKCA.2007.7.5.138
  3. B. I. Ahn, Location-based services of the mobile service. KT Economic Research, Trend Report, 2011.
  4. eMarketer, Social Network Ad Revenues Worldwide. Market Trend Report, 2012.
  5. J. Y. Oh, Comparison analysis of Korean, American and Japanese's SNS Service. National Information Society Agency, IT Policy Research Series, Vol. 11, 2009.
  6. S. Y. Kim, Pronoun of LBS the change of Foursquare. KT Economic Research, Trend Report, 2012.
  7. S. B. Cha, An analysis of Structural equation models on university students' Social Network Service participation and learning outcomes. Konkuk University, 2011.
  8. H. C. Shin, A location-based collaborative filtering recommender using quadtree. Yonsei University, 2011.
  9. S. H. Lee, B. K. Kim, T. B. Yoon and J. H. Lee, The method for extraction of meaningful places based on behavior Information of user, Korean Institute of Intelligent Systems, Vol. 20, No. 4, pp. 503-508, 2010. https://doi.org/10.5391/JKIIS.2010.20.4.503
  10. S. J. Bae, Study of personalized recommendation algorithms for new books on internet bookstores. Chungnam University, 2003.