Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

With the development of services computing and cloud computing, number of Web services has increased rapidly, and it becomes quite popular for developers to combine different Web services to build innovative Mash up applications. How to quickly locate desired Web service for developers, however, is still a challenging problem that needs to be addressed. Most existing related work employed keyword-based method to search services and focused on matching users' queries with semantic or syntactic Web service description. They seldom took advantage of relationships between services to improve the performance of service searching. This paper presents a correlation search method by making use of several relationships between Web services, to recommend a user with services that are similar, composable or potentially composable to a target service. One important advantage of this method is that it can guide users to find desired services promptly, and thus improves efficiency of the service discovery process. To mine the different relationships between services, several efficient algorithms are presented. Case studies and experiments show, the above correlation search method not only can recommend Web services to users that are relevant to the users' interest, but also can predict composable relationships between services with high performance.