Skip to main content

Recommendation in Reciprocal and Bipartite Social Networks–A Case Study of Online Dating

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7812)

Abstract

Many social networks in our daily life are bipartite networks that are built on reciprocity. How can we recommend users/friends to a user, so that the user is interested in and attractive to recommended users? In this research, we propose a new collaborative filtering model to improve user recommendations in reciprocal and bipartite social networks. The model considers a user’s “taste” in picking others and “attractiveness” in being picked by others. A case study of an online dating network shows that the new model outperforms a baseline collaborative filtering model on recommending both initial contacts and reciprocal contacts.

Keywords

  • bipartite social network
  • reciprocity
  • online dating
  • user recommendation

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-37210-0_25
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-37210-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhao, K., Kumar, A.: Who blogs what: understanding the publishing behavior of bloggers. World Wide Web Online First, 1–24 (2012)

    Google Scholar 

  2. Newman, M.: Clustering and preferential attachment in growing networks. Physical Review E 64, 025102 (2001)

    Google Scholar 

  3. Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. ACM, 641–650 (2010)

    Google Scholar 

  4. Zhao, K., Yen, J., Ngamassi, L.M., Maitland, C., Tapia, A.: Simulating inter-organizational collaboration network: a multi-relational and event-based approach. Simulation 88, 617–631 (2012)

    CrossRef  Google Scholar 

  5. Hopcroft, J., Lou, T., Tang, J.: Who will follow you back?: reciprocal relationship prediction, 2063740. ACM, 1137–1146 (2011)

    Google Scholar 

  6. Huang, Z., Zeng, D.D.: Why does collaborative filtering work? transaction-based recommendation model validation and selection by analyzing bipartite random graphs. Informs Journal on Computing 23, 138–152 (2011)

    MATH  CrossRef  Google Scholar 

  7. Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., Mahidadia, A.: Reciprocal and Heterogeneous Link Prediction in Social Networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 193–204. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  8. Madden, M., Lenhart, A.: Online dating. Technical report, Pew Research Center (2006), http://www.pewinternet.org/~/media//Files/Reports/2006/PIP_Online_Dating.pdf.pdf

  9. Abbott, M.: Internet dating defies economic gloom. BBC (December 2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yu, M., Zhao, K., Yen, J., Kreager, D. (2013). Recommendation in Reciprocal and Bipartite Social Networks–A Case Study of Online Dating. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37210-0_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37209-4

  • Online ISBN: 978-3-642-37210-0

  • eBook Packages: Computer ScienceComputer Science (R0)