Abstract: The use of Web services for various applications has led to the growth of web services on a large scale. Due to the increase in usage of web services, it has become of prime importance to design systems for effective web service recommendation. In our project, we propose a system for effective web service recommendations incorporating usersí preferences regarding quality and diversities amongst web services. Usersí requirements are considered and mined from his usage history. Then we find functional similarities using clustering techniques followed by applying a ranking algorithm to list top-k services. To discover high quality Web services, a number of QoS models for Web services and QoS-driven service selection approaches have been proposed in the service computing field. In this system user explicitly specifies his/her interests and QoS requirements, and submits them to the service discovery system. Then the service discovery system matches the userís interests and QoS requirements with corresponding attributes of Web services, and returns those with the best matching degrees to the user.

Keywords: Web Service Recommendation, ANN, feature evaluation, ranking, top-services.