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A Novel Framework for Service Set Recommendation in Mashup Creation

With an overwhelming number of web services online, recommending services for automatic mashup creation greatly facilitates the composition process of developers. Various approaches have been proposed for the task. However, these approaches concentrate on improving the recommending accuracy of an individual service, which give rise to two problems: (1) Top-ranked services may be highly redundant with the same functionality, and (2) The cooperation relations among services are ignored. Therefore, we argue that services should be recommended not individually, but collectively. In this paper, we focus on the problem of recommending service sets instead of services. A service set contains a list of functionally distinct services that collectively match different aspects of functional requirements and are more inclined to compose together following mashup composition patterns. To this end, we propose a novel recommendation framework consisting of two stages: Service Set Generation Stage and Service Set Ranking Stage. We also perform an experimental evaluation on ProgrammableWeb dataset to demonstrate the effectiveness of our framework.