Web Service Recommendation via Exploiting Location and QoS Information

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. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Webservices is steadily increasing on 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 serviceselection 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 and QoS values to cluster users and services, and makes personalized service recommendation for users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on therecommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 millionQoS records of real-world Web services to demonstrate the effectiveness of our approach.

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