Advertisement

Personal-Hosting RESTful Web Services for Social Network Based Recommendation

  • Youliang Zhong
  • Weiliang Zhao
  • Jian Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)

Abstract

Recommender systems have been widely used in information filtering. However the existing recommendation methods do not work effectively in the situations when a group of people want to share information and make recommendations within a social network. In this paper we propose a personal-hosting web services architecture ph-REST for social network based recommendation, in which every user is represented by a dedicated RESTful web services engine that collaborates with others over a social structure formed by co-peers with common interests. The proposed architecture explores the potential of applying service and Cloud computing to personal and social information sharing and assimilation.

Keywords

Personal-hosting web services Social network based recommendation Recommendation methods 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the State-of-the-Art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Alonso, G.: Web services: concepts, architectures and applications. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  3. 3.
    Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern information retrieval, vol. 463. ACM press, New York (1999)Google Scholar
  4. 4.
    Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Communications of the ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  5. 5.
    Berkovsky, S., Busetta, P., Eytani, Y., Kuflik, T., Ricci, F.: Collaborative Filtering over Distributed Environment. In: DASUM Workshop, Citeseer (2005)Google Scholar
  6. 6.
    Fielding, R.T.: Architectural styles and the design of network-based software architectures. PhD thesis, University of California, Irvine (2000)Google Scholar
  7. 7.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval 4(2), 133–151 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Gong, S.J., Ye, H.W., Su, P.: A Peer-to-Peer based distributed collaborative filtering architecture. In: International Joint Conference On Artificial Intelligence, pp. 305–307 (2009)Google Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Filannino, M., Molino, P.: Introducing serendipity in a content-based recommender system. In: International Conference on Hybrid Intelligent Systems, pp. 168–173. IEEE (2008)Google Scholar
  11. 11.
    ISI-WoK (2010), http://wokinfo.com/
  12. 12.
    Lemire, D., Maclachlan, A.: Slope one predictors for online Rating-Based collaborative filtering. Society for Industrial Mathematics (2005)Google Scholar
  13. 13.
    Liu, Z., Qu, W., Li, H., Xie, C.: A hybrid collaborative filtering recommendation mechanism for p2p networks. Future Gener. Comput. Syst. 26, 1409–1417 (2010)CrossRefGoogle Scholar
  14. 14.
    Milanovic, N.: Service engineering design patterns. In: International Workshop Service-Oriented System Engineering, pp. 19–26. IEEE (2006)Google Scholar
  15. 15.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)Google Scholar
  16. 16.
    Sen, S., Geyer, W., Muller, M., Moore, M., Brownholtz, B., Wilcox, E., Millen, D.R.: FeedMe: a collaborative alert filtering system. In: Anniversary Conference on Computer Supported Cooperative Work, pp. 89–98. ACM (2006)Google Scholar
  17. 17.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word-of-mouth. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co. (1995)Google Scholar
  18. 18.
    Tveit, A.: Peer-to-peer based recommendations for mobile commerce. In: International Workshop on Mobile Commerce, pp. 26–29. ACM, Rome (2001)Google Scholar
  19. 19.
    Xu, J., Zhang, L.J., Lu, H., Li, Y.: The development and prospect of personalized TV program recommendation systems. In: International Symposium on Multimedia Software Engineering, pp. 82–89. IEEE (2002)Google Scholar
  20. 20.
    Zadel, M., Fujinaga, I.: Web Services for Music Information Retrieval (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Youliang Zhong
    • 1
  • Weiliang Zhao
    • 1
  • Jian Yang
    • 1
  1. 1.Department of ComputingMacquarie UniversityNorth RydeAustralia

Personalised recommendations