Generating Accurate Predictions for Unknown QoS Web Services

Miss.Ashwini Sureshrao Puri, Prof. Mansi Bhonsle

Abstract


QoS-based Web service recommendation has recently gained much attention for providing a promising way
to help users find high-quality services. Collaborative filtering
is an important method for predicting missing values, and
has thus been widely adopted in the prediction of unknown
QoS values.However, collaborative filtering originated from the
processing of subjective data, such as movie scores. The QoS
data of Web services are usually objective, meaning that existing
collaborative filtering-based approaches are not always applicable
for unknown QoS values. Based on real world Web service QoS
data and a number of experiments, in this paper, we determine
some important characteristics of objective QoS datasets that
have never been found before. We propose a prediction algorithm
to realize these characteristics, allowing the unknown QoS values
to be predicted accurately. Experimental results show that the
proposed algorithm predicts unknown Web service QoS values
more accurately than other existing approaches.Comprehensive
experiments are conducted using more than 1.5 million QoS
records of real-world web service invocations. The experimental
results show the efficiency and effectiveness of our approach.

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