Mashup Service Recommendation Based on Usage History and Service Network

Buqing Cao, Jianxun Liu, Mingdong Tang, Zibin Zheng and Guangrong Wang
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Buqing Cao: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
Jianxun Liu: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
Mingdong Tang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
Zibin Zheng: Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, China
Guangrong Wang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

International Journal of Web Services Research (IJWSR), 2013, vol. 10, issue 4, 82-101

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.

Date: 2013
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