IMPROVED FRAMEWORK FOR DIVERSIFYING WEB SERVICE RECOMMENDATION RESULTS USING USERS REVIEWS AND USAGE HISTORY

As the increasing web services day by day over the internet, discovery of web services is becoming challenging research problem to be addressed for service computing community. There are number of web service recommendation methods has been proposed so far to solve the problem of web service discovery from the large pool of web services. However the limitation of these methods is that they are producing the similar web services in recommendation lists some times. To address this research problem, the novel improved web service recommendation method is presented in this paper. This approach is mainly dealing to produce the diversity in results of web service recommendation. In this paper, functional interest, QoS preference and diversity features are combined to produce the unique recommendation list of web services to end users. To produce the unique recommendation results, In this paper use proposed diversified web service ranking method which is based on web services functional relevance such as non-functional relevance, historical user interest relevance, potential user interest relevance etc. Additionally to improve the performance, designed new algorithm name as user relevance reviews. This method helps to improve the quality, accuracy of web service recommendation result