Predicting New User’s Behavior in Online Dating Systems

  • Tingting Wang
  • Hongyan Liu
  • Jun He
  • Xuan Jiang
  • Xiaoyong Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7121)

Abstract

Predicting new user’s reaction behavior to its recommended candidate partner correctly is critical to improve recommendation accuracy in online dating systems. However, new user (cold start) problem and data sparseness problem in the online dating system make this task very challenging. In this paper, we propose a hybrid method called crowd wisdom based behavior prediction to solve the two problems and achieve good prediction accuracy. By this method, old users who have been recommended partners before are first separated into groups. Users in each group have similar preference for partners. Then, we propose a novel measure to combine a group user’s collective behavior to predict one user’s behavior, which can solve the data sparseness problem. By calculating the probability a new user belongs to each group and utilizing the group’s behavior we can solve the new user problem. Based on these strategies, we develop a behavior prediction algorithm for new users. Experimental results conducted on a real online dating dataset show that our proposed method performs better than other traditional methods.

Keywords

online dating recommendation clustering classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mooney, R.J., Bennett, P.N., Roy, L.: Book Recommending Using Text Categorization with Extracted Information. In: Proc. Recommender Systems Papers from 1998 Workshop, Technical Repot WS-98-08 (1998)Google Scholar
  2. 2.
    Krzywicki, A., Wobcke, W., Cai, X., Mahidadia, A., Bain, M., Compton, P., Kim, Y.S.: Interaction-Based Collaborative Filtering Methods for Recommendation in Online DatingGoogle Scholar
  3. 3.
    Kazienko, P., Musial, K.: Recommendation FrameWork for Online Social Networks. In: The 4th Atlantic Web Intelligence Conference (AWIC 2006), pp. 110–120. Springer, Washington D.C (2006)Google Scholar
  4. 4.
    Chen, L., Nayak, R., Xu, Y.: Improving Matching Process in Social Network. In: 2010 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 305–311 (2010)Google Scholar
  5. 5.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 331–370 (2002)Google Scholar
  6. 6.
    Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Computing 48(1), 96–129 (1998)CrossRefMATHGoogle Scholar
  7. 7.
    Karypis, G., Kumar, V.: METIS: Unstructured Graph Partitioning and Sparse Matrix Ordering System. Technical Report, Department of Computer Science, University of Minnesota (1995)Google Scholar
  8. 8.
    Nayak, R.: Utilizing Past Relations and User Similarities in a Social Matching System. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 99–110. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Nayak, R., Zhang, M., Chen, L.: A Social Matching System for an Online Dating Network: A Preliminary Study. In: IEEE International Conference on Data Mining Workshops, ICDMW 2010, pp. 352–357 (201)Google Scholar
  10. 10.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005), doi:10.1109/TKDE.2005.99CrossRefGoogle Scholar
  11. 11.
    Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999), doi:10.1023/A:1006544522159CrossRefGoogle Scholar
  12. 12.
    de Gemmis, M., Iaquinta, L., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Preference Learning in Recommender Systems. In: ECML/PKDD 2009 Workshop on Preference Learning, PL 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tingting Wang
    • 1
  • Hongyan Liu
    • 2
  • Jun He
    • 1
  • Xuan Jiang
    • 1
  • Xiaoyong Du
    • 1
  1. 1.Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, School of InformationRenmin University of ChinaChina
  2. 2.Department of Management Science and EngineeringTsinghua UniversityChina

Personalised recommendations