Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers

  • Liang Wu
  • Alvin Chin
  • Guandong Xu
  • Liang Du
  • Xia Wang
  • Kangjian Meng
  • Yonggang Guo
  • Yuanchun Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7826)


WuXianGouXiang is an O2O(offline to online and vice versa)-based mobile application that recommends the nearby coupons and deals for users, by which users can also follow the shops they are interested in. If the potential followers of a shop can be discovered, the merchant’s targeted advertising can be more effective and the recommendations for users will also be improved. In this paper, we propose to predict the link relations between users and shops based on the following behavior. In order to better model the characteristics of the shops, we first adopt Topic Modeling to analyze the semantics of their descriptions and then propose a novel approach, named INtent Induced Topic Search (INITS) to update the hidden topics of the shops with and without a description. In addition, we leverage the user logs and search engine results to get the similarity between users and shops. Then we adopt the latent factor model to calculate the similarity between users and shops, in which we use the multiple information sources to regularize the factorization. The experimental results demonstrate that the proposed approach is effective for detecting followers of the shops and the INITS model is useful for shop topic inference.


User Behavior Location Based Services Matrix Factorization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: ACM International Conference on Web Search and Data Mining, pp. 635–644 (2011)Google Scholar
  2. 2.
    Baeza-yates, R.A., Ribeiro-neto, B.A.: Modern Information Retrieval (1999)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46, 604–632 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Leskovec, J., Huttenlocher, D.P., Kleinberg, J.M.: Predicting positive and negative links in online social networks. Computing Research Repository abs/1003.2:641–650 (2010)Google Scholar
  6. 6.
    Li, Y., Hu, J., Zhai, C., Chen, Y.: Improving one-class collaborative filtering by incorporating rich user information. In: International Conference on Information and Knowledge Management, pp. 959–968 (2010)Google Scholar
  7. 7.
    Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: International Conference on Information and Knowledge Management, pp. 556–559 (2003)Google Scholar
  8. 8.
    Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Knowledge Discovery and Data Mining (2010)Google Scholar
  9. 9.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to information retrieval (2008)Google Scholar
  10. 10.
    Newman, M.E.J.: Clustering and preferential attachment in growing networks. Physical Review E 64 (2001)Google Scholar
  11. 11.
    Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Knowledge Discovery and Data Mining, pp. 650–658 (2008)Google Scholar
  12. 12.
    Srebro, N., Jaakkola, T.: Weighted Low-Rank Approximations. In: International Conference on Machine Learning, pp. 720–727 (2003)Google Scholar
  13. 13.
    Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: International Conference on Data Mining, pp. 322–331 (2007)Google Scholar
  14. 14.
    Xu, Z., Kersting, K., Tresp, V.: Multi-Relational Learning with Gaussian Processes. In: International Joint Conference on Artificial Intelligence, pp. 1309–1314 (2009)Google Scholar
  15. 15.
    Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach. In: National Conference on Artificial Intelligence (2010)Google Scholar
  16. 16.
    Yoon, H., Zheng, Y., Xie, X., Woo, W.: Smart Itinerary Recommendation Based on User-Generated GPS Trajectories (2010)Google Scholar
  17. 17.
    Zheng, Y., Xie, X.: Learning Travel Recommendations from User-Generated GPS Traces 2, 1–29 (2011)Google Scholar
  18. 18.
    Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: World Wide Web (2010)Google Scholar
  19. 19.
    Park, M.-H., Hong, J.-H., Cho, S.-B.: Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 1130–1139. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liang Wu
    • 2
  • Alvin Chin
    • 1
  • Guandong Xu
    • 3
  • Liang Du
    • 5
  • Xia Wang
    • 4
  • Kangjian Meng
    • 4
  • Yonggang Guo
    • 4
  • Yuanchun Zhou
    • 2
  1. 1.Xpress Internet Services, NokiaBeijingChina
  2. 2.Computer Network Information CenterChinese Academy of SciencesChina
  3. 3.Advanced Analytics InstituteUniversity of Technology SydneyAustralia
  4. 4.Beijing NoyaXe Technologies Co. Ltd.China
  5. 5.Institute of SoftwareChinese Academy of SciencesChina

Personalised recommendations