Mashup Service Recommendation Based on Usage History and Service Network

Abstract

With the rapid development of Web2.0 and its related technologies, Mashup services (i.e., Web applications created by combining two or more Web APIs) are becoming a hot research topic. The explosion of Mashup services, especially the functionally similar or equivalent services, however, make services discovery more difficult than ever. In this paper, we present an approach to recommend Mashup services to users based on usage history and service network. This approach firstly extracts users' interests from their Mashup service usage history and builds a service network based on social relationships information among Mashup services, Web application programming interfaces (APIs) and their tags. The approach then leverages the target user's interest and the service social relationship to perform Mashup service recommendation. Large-scale experiments based on a real-world Mashup service dataset show that the authors' proposed approach can effectively recommend Mashup services to users with excellent performance. Moreover, a Mashup service recommendation prototype system is developed.