Instant Recommendation for Web Services Composition

Web service composition helps users integrate services to create new large-granularity and value-added composite services. Most recent studies have focused on automatic AI-Planning-based static or dynamic composition at functional- or process-level. However in industry, most business applications are still composed manually or semi-automatically with abundant domain expertise. Consequently, to build a good and reliable composite service is really a time-consuming and professional task. Inspired by the Instant Search of Google, we propose an Instant recommendation approach to provide optimal suggestions while a composition process incrementally proceeds. In our model, we fully utilize the execution log of composite services, and intend to identify appropriate services which have been proved to be more reliable and robust, therefore those services have higher probability to fulfill users´ demands. To find the top-k possible composite services in real-time, we adopt the A* search algorithm with various pruning heuristics to dynamically expand the search space efficiently. Experiments on a real-world dataset with 15,959 real Web services crawled from the Internet demonstrate the effectiveness and efficiency of the proposed approach.