With the wide application of service technologies in various fields, the number of services is increasing dramatically. So, a function is often provided by many services of different QoS (quality of service). Thus, due to the lack of professional knowledge, users often face great difficulties in finding the right services they really need. Hence, accurate and efficient service recommendation is not just an effective way of service advertising, but also an important means to promote user experience. Most of traditional service recommendation methods are based on predictions of QoS values. However, because of the dynamic nature of the Internet, it is hard to guarantee the predicted values are consistent with the actual values. This article proposes a granule distribution-aware SVM (support vector machine) model for service recommendation, namely GDSVM4SR. It takes advantages of granular computing to identify similar users, refine the training service set, and decrease the influence of unfaithful ratings. And then, GDSVM4SR trains an SVM separating hyperplane to sort unknown services and generate recommendations, thus it can avoid the prediction of QoS values. Experimental results show that the proposed GDSVM4SR outperforms several state-of-the-art methods in terms of the efficiency and the precision of recommendation.

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.

With increasing adoption and presence of Web services, service composition becomes an effective way to construct software applications. Composite services need to satisfy both the functional and the non-functional requirements. Traditional methods usually assume that the quality of service (QoS) and the behaviors of services are deterministic, and they execute the composite service after all the component services are selected. It is difficult to guarantee the satisfaction of user constraints and the successful execution of the composite service. This paper models the constraint-satisfied service composition (CSSC) problem as a Markov decision process (MDP), namely CSSC-MDP, and designs a Q-learning algorithm to solve the model. CSSC-MDP takes the uncertainty of QoS and service behavior into account, and selects a component service after the execution of previous services. Thus, CSSC-MDP can select the globally optimal service based on the constraints which need the following services to satisfy. In the case of selected service failure, CSSC-MDP can timely provide the optimal alternative service. Simulation experiments show that the proposed method can successfully solve the CSSC problem of different sizes. Comparing with three representative methods, CSSC-MDP has obvious advantages, especially in terms of the success rate of service composition.