LAUSR

The goal of service selection is to select services that satisfy user’s requirements from candidate services with the same function and different qualities of service (QoS). Traditional service selection methods require users to provide the weight for each QoS attribute, but users sometimes cannot provide accurate weights in practice. Moreover, because service providers may be untrustworthy, they may not provide reliable QoS values. Under these situations, the traditional service selection methods will be not very effective. To address this problem, we propose a service selection method based on ordinal classification for historical records. In this method, both QoS attributes and user-given ratings in historical records are considered as ordinal values, and an ordinal classification model will be learned from these data. For the historical records without user-given ratings, their ratings can be predicted by this model. Finally the services will be selected based on these ratings. The proposed method can work well even without the QoS attribute weights provided by user and the QoS values provided by service provider. We compare the proposed method with three weighted-based service selection methods and eight classification-based methods, which demonstrates that the proposed method can obtain better service selection results and have the best robustness.