(untitled) (JOURNAL OF ADVANCES IN CHEMISTRY, 2017)
Web services are integrated software components for the support of interoperable machine to machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems to the resulting applications. In this paper, a novel collaborative filtering-based Web service recommender system is proposed to help the users and select services with optimal QoS performance. Our recommender system employ an effective and efficient selection of web services and relevant retrieval of information and makes personalized service recommendation to users based on the clustering results. Compared with existing service recommendation methods, the proposed approach achieves considerable improvement on the recommendation accuracy and the QoS performance metrics adopted in this paper shows the better accuracy and relevant web services.