A Web Service Recommendation Approach Based on QoS Prediction Using Fuzzy Clustering

Web services, as loosely-coupled software systems, are increasingly being published to the web and there are a large number of services with similar functions. Therefore, service users compare the non-functional properties of services, e.g., Quality of Service (QoS), when they make service selection. This paper aims at generating a more comprehensive web service recommendation to users with a novel approach to fulfill more accurate prediction of unknown services´ QoS values. We accomplish the QoS prediction by using fuzzy clustering method with calculating the users´ similarity. Our approach improves the prediction accuracy and this is confirmed by comparing experiments with other methods. In addition, the quality of web services is considered as a multi-dimensional object, and each dimension is one aspect of the web service´s non-functional properties. We also provide an application example to demonstrate how to utilize our approach to rank services by a score function and map multi-dimensional QoS properties into a single dimensional value.