Regression-Based Bootstrapping of Web Service Reputation Measurement

In the literature, many solutions for measuring the reputation of web services have been proposed. These solutions help in building service recommendation systems. Nonetheless, there are still many challenges that need to be addressed in this context, such as the "cold start" problem, and the lack of estimation of the initial reputation values of newcomer web services. As reputation measurement depends on the previous reputation values, the lack of initial values can subvert the performance of the whole service recommendation system, making it vulnerable to different threats, like the Sybil attack. In this paper, we propose a new bootstrapping mechanism for evaluating the reputation of newcomer web services based on their initial Quality of Service (QoS) attributes, and their similarity with "long-standing" web services. Basically, the technique uses regression models for estimating the unknown reputation values of newcomer services from their known values of QoS attributes. The technique has been experimented on a large set of services, and its performance has been measured using some statistical metrics, such as the coefficient of determination (R2), Mean Absolute Error (MSE), and Percentage Error (PE).