Collaborative Prediction of Web Service Quality Based on User Preference and Service

Authors

  • Zhanglong Nie School of Software and Big Data, Changzhou College of Information Technology, Changzhou, 213164, China
  • Yang Song State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract

The prediction of Web service quality plays an important role in improving user services. Therefore, it has been one of the most popular topics in the field of Internet services. In traditional collaborative filtering methods, the differences in personalization and preferences of different users have been ignored. In this paper, for different types of quality of service (QoS) attributes, different extraction rules are applied to extract the user preference matrices from the original Web data, and the negative value filtering-based Top-K method is used to merge the optimization results into the collaborative prediction method. In doing so, the individualized differences have been fully exploited, and the problem of inconsistent QoS values has been resolved. The experimental results demonstrate the validity of the proposed method. Compared with previous methods, the proposed method
performs better, and the results are closer to the real values.

Keywords: web service; quality of service; collaborative filtering; user preferences; Internet

Cite As

Y. Song, Z. Nie, “Collaborative Prediction of Web Service Quality Based on User Preference and Serviceâ€, Engineering Intelligent Systems, vol. 28 no. 1, pp. 31-40, 2020.

 

 

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Published

2020-03-01

Issue

Vol. 28 No. 1 (2020): International Journal of Engineering Intelligent Systems

Section

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