QoS-Based Web Service Ranking Model Considering Decision Making Methods

For selecting from a list of functionally similar services, users regularly decide based on multiple Quality of Service (QoS) criteria which they require on the target service. In selection process, different decision making strategies (compensatory and non-compensatory) are followed by various users. Recent works on QoS-based service selection systems are not considering these decision strategies while ranking the similar services, which are vital to generate exact ranking for individual users. This paper proposes a novel service ranking model based on decision strategy. Furthermore, since different users follow diverse strategies in various contexts at different times, a learning to rank algorithm is used to learn a personalized ranking model for individual users constructed on how services are selected by them in the previous usage and the observed results shows an improvement with respect to accuracy in ranking web services.