Mashup Service Classification and Recommendation Based on Similarity Computing

Abstract

Because of the excellent performance of Mashup service in the service composition, Mashup service is used more and more. It is meaningful for service management, discovery and composition that how to achieve effective Mashup service classification and recommendation. We analyze the service network consisted of Mashup applications, Web API services and Tag functions, basing on the rule that there are connections among those Mashups if some Mashups call the same APIs and are marked by the same Tags, and the degree of the connection can be described by similarity, and build 13 kinds of networks and visualize them. Based on built service network, this paper proposes an automatic service classification algorithm that each connected sub-graph is justly a classification in the network consisted of a same kind of service node, and a service recommendation method based on the similarity sorting. We use the Web API data crawled from ProgrammableWeb. The result of our experiment shows the composite index of precision rate and recall rate is up to 87.44%.