Web Service Instant Recommendation for Sustainable Service Mashup

Feng Zhang, Benming Chen and Cong Liu
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Feng Zhang: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Benming Chen: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Cong Liu: School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China

Sustainability, 2020, vol. 12, issue 20, 1-18

Abstract: Service Mashups can help users to integrate data of multiple sources based on Web services composition. Considering a kind of sustainable service Mashup whose data requirement cannot be predetermined, so users need to choose and compose services in a tentative manner. Meanwhile, users can choose and compose services continually to obtain more data based on existing composition results. In such Mashups, a Web service is chosen according to the data provided by the service. Because it is difficult for users to choose from large amounts of services manually, it is a challenge to recommend services instantly for users during the construction of a sustainable service Mashup. This paper proposes an approach to recommend Web services instantly for a sustainable service Mashup. According to the services used in the service Mashup under construction, candidate services are chosen based on the Mashups that are similar to the constructing Mashup, as well as the parameter correlations of services from the perspective of actual operations of Web service composition. Experimental results indicate that the proposed approach has better precision, recall, and coverage values compared to existing state-of-the-art approaches, and therefore, it is more suitable for instant service recommendation of sustainable service Mashups.

Keywords: sustainable service Mashup; Mashup similarity; parameter correlation; Web service recommendation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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