Personalized QoS-Aware Web Service Recommendation via Exploiting Location and Collaborative Filtering

A web service is a software system designed to support 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 Web services 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 service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. This paper, propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. 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. Different from previous work, this approach employs the characteristic of QoS and achieves considerable improvement on the recommendation accuracy.