LLCF: A Load- and Location-Aware Collaborative Filtering Algorithm to Predict QoS of Web Service

Abstract

The prediction of Quality of Service (QoS) significantly facilitates the web services selection for QoS based web service recommender systems. One effective method for predicting web services' QoS values is the collaborative filtering (CF) algorithm. However, the existing CF algorithms experience potential scalability issues, as well as the accuracy issues. We present a load- and location-aware collaborative filtering algorithm (LLCF) to improve the prediction accuracy and the scalability. To assess the proposed LLCF, we leverage Amazon Cloud platform where hosts various web services. The experiments are conducted based on selected web services where QoS values are collected. The results show the prediction accuracy is significantly improved by the proposed LLCF. Furthermore, complexity analysis results show that our LLCF can remarkably improve the scalability.