Web Service Recommendation via Exploiting Location and QoS Information

Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network to provide specific services. Web services have been widely employed for building service-oriented applications from both industry and academia in recent years. The number of publicly available Web services is steadily increasing in the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. Our recommender system employs the location information as well as QoS values to cluster users and services, and makes personalized recommendation of optimal services to users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on the recommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 million QoS records of real world Web services to demonstrate the effectiveness of the proposed approach.