NDLI: User-QoS-Based Web Service Clustering for QoS Prediction

Content Provider	IEEE Xplore Digital Library
Author	Fuxin Chen Shijin Yuan Bin Mu
Copyright Year	2015
Description	Author affiliation: Sch. of Software Eng., Tongji Univ., Shanghai, China (Fuxin Chen; Shijin Yuan; Bin Mu)
Abstract	QoS prediction has become an important step in service recommending and selecting. Most QoS prediction approaches are using collaborative filtering as a prediction technique. But collaborative filtering may suffer from data sparsity problem which degrade the prediction accuracy. In order to alleviate the data sparsity problem of collaborative filtering, we presented a hybrid QoS prediction approach by applying clustering on web services before applying collaborative filtering (named services clustering QoS prediction, SCQP). The clustering process cluster web services in to service clusters in which services have the same physical environment. Then the similarity between users is calculated based on these service clusters instead of individual services. So that there are more information to be used when calculate the similarity and it will contribute to elevate the prediction precision. The experimental results showed that our hybrid approach could not only achieve higher prediction precision, but also reduce the computation time than other collaborative filtering based prediction methods.
Starting Page	583
Ending Page	590
File Size	355141
Page Count	8
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quality of service Web services Collaboration Clustering algorithms Filtering Prediction algorithms Accuracy collaborative filtering Web Service QoS prediction data sparsity clustering
Content Type	Text
Resource Type	Article

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