GeoSocialRec: Explaining Recommendations in Location-Based Social Networks

  • Panagiotis Symeonidis
  • Antonis Krinis
  • Yannis Manolopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8133)


Social networks have evolved with the combination of geographical data, into location-based social networks (LBSNs). LBSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. In this paper, we have implemented an online recommender system for LBSNs, called GeoSocialRec, where users can get explanations along with the recommendations on friends, locations and activities. We have conducted a user study, which shows that users tend to prefer their friends opinion more than the overall users’ opinion. Moreover, in friend recommendation, the users’ favorite explanation style is the one that presents all human chains (i.e. pathways of more than length 2) that connect a person with his candidate friends.


Recommender System Target User Activity Recommendation Explanation Style Location Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th International Conference on World Wide Web (WWW), New York, NY, pp. 61–70 (2010)Google Scholar
  2. 2.
    Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Social tagging for personalized location-based services. In: Proceedings of the 2nd International Workshop on Social Recommender Systems (2011)Google Scholar
  3. 3.
    Brand, M.: Incremental singular value decomposition of uncertain data with missing values. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 707–720. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Economist. A world of connections: A special report on networking. The Economist: Editorial Team (2010)Google Scholar
  5. 5.
    Leung, K., Lee, D.L., Lee, W.C.: Clr: a collaborative location recommendation framework based on co-clustering. In: Proceedings of the 34th International ACM Conference on Research and Development in Information Retrieval (SIGIR), New York, NY, pp. 305–314 (2011)Google Scholar
  6. 6.
    Monge, P.R., Contractor, N.: Theories of communication networks. Oxford University Press (2003)Google Scholar
  7. 7.
    Papadimitriou, A., Symeonidis, P., Manolopoulos, Y.: Friendlink: Predicting links in social networks via bounded local path traversal. In: Proceedings of the 3rd Conference on Computational Aspects of Social Networks (CASON), Salamanca, Spain (2011)Google Scholar
  8. 8.
    Quercia, D., Lathia, N., Calabrese, F., Di Lorenzo, G., Crowcroft, J.: Recommending Social Events from Mobile Phone Location Data. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), Sydney, Australia, pp. 971–976 (2010)Google Scholar
  9. 9.
    Sarwar, B., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: International Conference on Computer and Information Science, Liverpool, UK (2002)Google Scholar
  10. 10.
    Scellato, S., Mascolo, C., Musolesi, M., Latora, V.: Distance Matters: Geo-social Metrics for Online Social Networks. In: Proceedings of the 3rd Workshop on Online Social Networks (WOSN), Boston, MA (2010)Google Scholar
  11. 11.
    Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM Conference on Research and Development in Information Retrieval (SIGIR), New York, NY, pp. 325–334 (2011)Google Scholar
  12. 12.
    Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference in Artificial Intelligence (AAAI), Atlanta, GA, pp. 236–241 (2010)Google Scholar
  13. 13.
    Zheng, W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th International Conference on World Wide Web (WWW), New York, NY, pp. 1029–1038 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Panagiotis Symeonidis
    • 1
  • Antonis Krinis
    • 1
  • Yannis Manolopoulos
    • 1
  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

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