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A Web Service Recommendation System Based on Users’ Reputations

  • Yu Furusawa
  • Yuta Sugiki
  • Reiko Hishiyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)

Abstract

In recent years, as the Internet spreads, the use of the Web Service has increased, and it has diversified. The Web Service is registered with UDDI, and the user selects service there and can use it for the provider by making a demand. In future, if the Web Service comes to be used more widely, the number of Web Services will increase, and the number of registrations at the UDDI will also increase. The user examines the large number of available services, and needs to choose the service that best matches their purpose. Quality of Service (QoS) is used as an index when a user chooses a service. Many studies show that the scoring of QoS for service selection is important. Quality of Service is registered by the provider and is treated as an objective factor. However, subjective evaluation, the evaluation of the user after the service use, is also needed to choose the best service. In this study, we use a new element, evaluation, in addition to QoS for service selection. We have expanded the existing filtering technique to make a new way of recommending services. Our method incorporates subjective evaluation. With this model, we apply the technique of information filtering to the Web Service recommendation and make an agent. Also, we simulate it after having clarified the behavior and tested it. The results of testing show that the model provides high levels of precision.

Keywords

Recommendation System Suggested Method Service Selection Collaborative Filter Service Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Au Yeung, C., Iwata, T.: Trust relation and product rating on the web. WebDB Forum (2010)Google Scholar
  2. 2.
    Erl, T.: Service-oriented architecture: concepts, technology, and design. Prentice Hall (2005)Google Scholar
  3. 3.
    Iwahama, K., Hijikata, Y., Nishida, S.: Content-based filtering system for music data. In: Application and the Internet Workshops, pp. 480–487 (2004)Google Scholar
  4. 4.
    Murakami, E., Terano, T.: Collaborative Filtering for a Distributed Smart IC Card System. In: Yuan, S.-T., Yokoo, M. (eds.) PRIMA 2001. LNCS (LNAI), vol. 2132, pp. 183–197. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufmann (1993)Google Scholar
  6. 6.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 1994), pp. 175–186 (1994)Google Scholar
  7. 7.
    Sha, L., Shaozhong, G., Xin, C., Mingjing, L.: A qos based web service selection model. In: International Forum on Information Technology and Applications (IFITA 2010), pp. 353–356 (2009)Google Scholar
  8. 8.
    Wang, X., Vitvar, T., Kerrigan, M., Toma, I.: A Qos-Aware Selection Model for Semantic Web Services. In: Dan, A., Lamersdorf, W. (eds.) ICSOC 2006. LNCS, vol. 4294, pp. 390–401. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Vassileva, J.: Toward trust and reputation based web service selection: a survey (2007), http://bistrica.usask.ca/madmuc/papers/yaojulita-ws-mas-survey.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yu Furusawa
    • 1
  • Yuta Sugiki
    • 1
  • Reiko Hishiyama
    • 1
  1. 1.Graduate School of Creative Science and EngineeringWaseda UniversityShinjuku-kuJapan

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