Web service QoS prediction: when collaborative filtering meets data fluctuating in big-range

Abstract

Service recommendation aims to help users to find the most suitable Web services based on their quality of service (QoS) preferences instead of searching through extensive volume of Web services using search engine manually. Accurate unknown QoS rating prediction is one of the key challenges in the analysis of service recommendation. Collaborative filtering (CF) is a well-known recommendation method that estimates missing ratings by employing a set of similar users to the active user. The core idea of CF consists of picking out an appropriate set of users and using them in the rating prediction process. However, the majority of existing CF methods are not well-designed for Web service QoS prediction as they ignore the implicit but important characteristic of Web service QoS data that fluctuate in big-range. In other words, through analysis of real-world QoS datasets, we observed that QoS ratings vary widely and they are highly skewed with large variances, as two main facts, which dramatically degrade the accuracy of CF methods in QoS prediction. Towards this problem, in this paper, we propose a big-range aware collaborative filtering approach dubbed BRACF to predict Web service QoS ratings accurately. Specifically, since big-range of QoS data can lead to similarity exaggeration, we design a simple yet effective similarity model which considers the influence of big-range among users’ QoS data for accurately characterizing the similarity between users. Moreover, the similarity model is seamlessly incorporated into CF model for identifying similar neighbor using Top-K strategy and then it generates QoS predictions by combining bias information. Through extensive experiments on two public real-world Web service QoS for datasets, as response time and throughput, we show that BRACF significantly outperforms state-of-the-art CF methods. We believe that this work demonstrates the potential impact of big range data for the accurate QoS prediction.