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RPCV: Recommend Potential Customers to Vendors in Location-Based Social Network

  • Yuanliu Liu
  • Pengpeng ZhaoEmail author
  • Victor S. Sheng
  • Zhixu Li
  • An Liu
  • Jian Wu
  • Zhiming Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Location-based social network has received much attention recently. It provides rich information of social and spatial context for researchers to study users’ behaviors from different aspects. A number of recent efforts focus on recommending locations, users, activities, and social medias for users. Unlike previous works, we intend to make recommendations for vendors, assisting vendors in finding potential customers in location-based social network. We propose a framework to recommend potential customers to vendors (called RPCV) in location-based social network effectively and efficiently. To find the best set of customers, RPCV takes both spatial relations and user preference into consideration. A reverse spatial-preference kRanks algorithm, which effectively combines spatial relations with user preference, is also proposed. Our experimental results on real datasets from Foursquare and Brightkite show that our framework has higher performance than other state-of-the-art approaches.

Keywords

Location based social network Recommendation 

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References

  1. 1.
    Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F.: Lars: a location-aware recommender system. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 450–461. IEEE (2012)Google Scholar
  2. 2.
    Zheng, V.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, pp. 1029–1038. ACM (2010)Google Scholar
  3. 3.
    Bouidghaghen, O., Tamine, L., Boughanem, M.: Personalizing mobile web search for location sensitive queries. In: 2011 12th IEEE International Conference on Mobile Data Management (MDM), vol. 1, pp. 110–118. IEEE (2011)Google Scholar
  4. 4.
    Ashbrook, D., Starner, T.: Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  5. 5.
    Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artificial Intelligence 171(5), 311–331 (2007)zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Lin, J., Xiang, G., Hong, J.I., Sadeh, N.: Modeling people’s place naming preferences in location sharing. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 75–84. ACM (2010)Google Scholar
  7. 7.
    Gao, H., Tang, J., Hu, X., Liu, H.: Modeling temporal effects of human mobile behavior on location-based social networks. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 1673–1678. ACM (2013)Google Scholar
  8. 8.
    Scellato, S., Mascolo, C., Musolesi, M., Latora, V.: Distance matters: geo-social metrics for online social networks. In: Proceedings of the 3rd Conference on Online Social Networks, pp. 8–8 (2010)Google Scholar
  9. 9.
    Scellato, S., Noulas, A., Lambiotte, R., Mascolo, C.: Socio-spatial properties of online location-based social networks. ICWSM 11, 329–336 (2011)Google Scholar
  10. 10.
    Ye, M., Yin, P., Lee, W.C.: Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 458–461. ACM (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 SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM (2011)Google Scholar
  12. 12.
    Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 671–680. ACM (2012)Google Scholar
  13. 13.
    Vlachou, A., Doulkeridis, C., Kotidis, Y., Norvag, K.: Reverse top-k queries. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE), pp. 365–376. IEEE (2010)Google Scholar
  14. 14.
    Yuan, S.T., Tsao, Y.W.: A recommendation mechanism for contextualized mobile advertising. Expert Systems with Applications 24(4), 399–414 (2003)CrossRefGoogle Scholar
  15. 15.
    Humphreys, L.: Mobile social networks and social practice: A case study of dodgeball. Journal of Computer-Mediated Communication 13(1), 341–360 (2007)CrossRefGoogle Scholar
  16. 16.
    Teh, Y.W.: A bayesian interpretation of interpolated kneser-ney (2006)Google Scholar
  17. 17.
    Pitman, J., Yor, M.: The two-parameter poisson-dirichlet distribution derived from a stable subordinator. The Annals of Probability, 855–900 (1997)Google Scholar
  18. 18.
    Van Dam, A., Feiner, S.K.: Computer graphics: principles and practice. Pearson Education (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yuanliu Liu
    • 1
  • Pengpeng Zhao
    • 1
    Email author
  • Victor S. Sheng
    • 2
  • Zhixu Li
    • 1
  • An Liu
    • 1
  • Jian Wu
    • 1
  • Zhiming Cui
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Computer Science DepartmentUniversity of Central ArkansasConwayUSA

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