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QoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression

  • Soumi ChattopadhyayEmail author
  • Ansuman Banerjee
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)

Abstract

With increasing demand and adoption of web services in the world wide web, selecting an appropriate web service for recommendation is becoming a challenging problem to address today. The Quality of Service (QoS) parameters, which essentially represent the performance of a web service, play a crucial role in web service selection. However, obtaining the exact value of a QoS parameter of service before its execution is impossible, due to the variation of the QoS parameter across time and users. Therefore, predicting the value of a QoS parameter has attracted significant research attention. In this paper, we consider the QoS prediction problem and propose a novel solution by leveraging the past information of service invocations. Our proposal, on one hand, is a combination of collaborative filtering and neural network-based regression model. Our filtering approach, on the other hand, is a coalition of the user-intensive and service-intensive models. In the first step of our approach, we generate a set of similar users on a set of similar services. We then employ a neural network-based regression module to predict the QoS value of a target service for a target user. The experiments are conducted on the WS-DREAM public benchmark dataset. Experimental results show the superiority of our method over state-of-the-art approaches.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Information TechnologyGuwahatiIndia
  2. 2.Indian Statistical InstituteKolkataIndia

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