Abstract:
More and more services with similar features are emerging day by day. Recommending personalized Web services to users has gathered significant attention. A recommender system intends to make recommendation of an item to a user by predicting user’s expected rating on that particular item.In order to predict unknown rating value , RS uses the existing rating values and supporting information. Quality of Web-service (QoS) have become an important parameter in determining the desired Web service for the user. These parameters involve response time and throughput, which varies greatly depending upon theWeb services contextual properties. One of the most commonly used Web services recommending techniques is Collaborative Filtering (CF). Current techniques are based on Collaborative Filtering, however, they do not efficiently incorporate the Web services’ context while making prediction of the QoS value. In this research work, an item-item collaborative filtering technique is developed for QoS prediction which uses the web services’ context for improvised neighborhood development. The context of aWeb services are defined by aWeb Serviced Description Language (WSDL) file and the WSDL file is processed to form a document of important words. Furthermore, the WSDL documents are clustered on the basis of the Term Frequency-Inverse Document Frequency (TF-IDF) values generated for words in a document. Numerous experiments have been performed for comparing other techniques to our technique, and the results prove our technique to be better among the others in term of accuracy and effectiveness by lowering the error rate.
