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Web service recommendation for mashup creation based on graph network

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Abstract

In recent years, the world has witnessed the increased maturity of service-oriented computing. The mashup, as one of the typical service-based applications, aggregates contents from more than one source into a single user interface. Facing the rapid growth of the number of web services, choosing appropriate web services for different mashup sources plays an important issue in mashup development, when, in particular, the new mashup is developed from the scratch. To solve this cold start problem when creating new mashups, we propose a web Service Recommendation approach for Mashup creation based on Graph network, called SRMG. SRMG makes service recommendation based on service characteristics and historical usage. It first leverages Bidirectional Encoder Representations from Transformers, to intelligently discover mashups with similar functionalities based on specifications. Afterward, it employs GraphGAN to obtain representation vectors for mashups and services based on historical usage, and further obtains mashup preferences for each service based on representation vectors. Finally, the new mashup’s preference for target services is derived from the preference of existing mashups that are similar to it. The extensive experiments on real datasets from ProgrammableWeb demonstrate that SRMG is superior to the state-of-the-art ones.

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Notes

  1. In this paper, we do not distinguish between API and service. They both refer a collection of functions for public usage.

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Acknowledgments

The authors wish to thank all the participants in the evaluation process for their help. This work was supported by the National Natural Science Foundation of China (No. 61702144), the Industrial Internet Innovation and Development Project of Ministry of Industry and Information Technology (Nos. TC200802G and TC2008033), the Key Research and Development Program of Zhejiang Province (No. 2020C01165), the Natural Science Foundation of Zhejiang Province (No. LQ20F020015) and the Scientific Research Foundation of Zhejiang Provincial Education Department (No. Y202250319).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Ting Yu, Dongjin Yu, Dongjing Wang & Xueyou Hu

  2. JiaXing Nanhu University, Jiaxing, 314001, China

    Ting Yu

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  1. Ting Yu
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Contributions

TY contributed to Methodology, Writing—review and editing. DY contributed to Writing—review and editing, Revision. DW contributed to Revision. XH: Revision.

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Correspondence to Dongjin Yu.

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Yu, T., Yu, D., Wang, D. et al. Web service recommendation for mashup creation based on graph network. J Supercomput 79, 8993–9020 (2023). https://doi.org/10.1007/s11227-022-05011-3

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