S-RAP: relevance-aware QoS prediction in web-services and user contexts

With quick advancement in web technology, web-services offered on internet are growing quickly, making it challenging for users to choose a web-service fit to their needs. Recommender systems save users the hassle of going through a range of products by product recommendations through analytical techniques on historical data of user experiences of the available items/products. Research efforts provide several methods for web-service recommendation in which QoS-related attributes play primary role such as response-time, throughput, security, privacy and web-service-delivery. Derivable attributes including, user-trustworthiness and web-services reputation in contexts of users and web-services can also affect the QoS prediction. The proposed research focuses on a web-service recommendation model, S-RAP, for QoS prediction based on derivable attributes to predict QoS of a web-service that a user who has not invoked it before would experience. Services-Relevance attribute is proposed in this publication, which emphasizes on employing the historical data and extracting the degree of relevance in the users and web-services context to predict the QoS values for a user. The proposed system produces satisfactorily accurate rating predictions in the experiments evaluated by the Mean Absolute Error and Normalized Mean Absolute Error metrics. The results compared with state-of-the-art models show a relative improvement by 4.0%.