Survey on recommendation and visualization techniques for QOS-aware web services

With the rapid growth of web services, maintaining QOS in providing the services is an important issue. QOS faces various factors like scalability, response time, service selection, quality control and so on. In this service selection and predicting for the best service is a challenge over the World Wide Web. Many approaches have been used to perform this task and the current approaches fail to consider the QOS variance according to user's location and lacks in transparency. So a novel collaborative filtering algorithm is designed for large-scale web service recommendations. For better understanding a recommendation visualization technique is used to show how the services are grouped based on user's choices.