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An API Recommendation Method Based on Beneficial Interaction

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2022)

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Abstract

With the wide application of Mashup technology, it has become one of the hot and challenging problems in the field of service computing that how to recommend the API to developers to satisfy their Mashup requirements. The existing service recommendation methods based on Graph Neural Network (GNN) usually construct feature interaction graph by the interactions of service features, and regard it as the input of GNN to achieve service prediction and recommendation. In fact, there are some distinctions in the interactions between service features, and the importance of interactions is also different. To address this problem, this paper proposes an API recommendation method based on beneficial feature interaction, which can distinguish and extract beneficial feature interaction pairs from a large number of service feature interaction relationships. Firstly, feature extraction of Mashup requirements and API services is performed, and the correlation between API services is calculated based on the label and description document of the API services and used as a basis for recommending API services to Mashup requirements. Secondly, edge prediction component is used to extract beneficial feature pairs from input features of Mashup requirements and API services to generate beneficial feature interaction diagram between features. Thirdly, the beneficial feature interaction diagram is used as input of the graph neural network to predict and generate the API services set of recommendations for the Mashup requirements. Finally, the experiment on ProgrammableWeb dataset shows that the AUC of the proposed method has increased 20%, 24%, 27%, 13% and 21% respectively than that of AFM, NFM, DeepFM, FLEN and DCN, which means the proposed method improves the accuracy and quality of service recommendation.

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Acknowledgment

Our work is supported by the National Natural Science Foundation of China (No. 61873316, 61872139, 61832014, and 61702181), the National Key R&D Program of China (No.2018YFB1402800), Hunan Provincial Natural Science Foundation of China under grant No. 2021JJ30274, and the Educational Commission of Hunan Province of China (No.20B244). Buqing Cao is the corresponding author of this paper.

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Authors and Affiliations

  1. School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology, Hunan University of Science and Technology, Xiangtan, Hunan, China

    Siyuan Wang, Buqing Cao, Xiang Xie, Lulu Zhang, Guosheng Kang & Jianxun Liu

Authors
  1. Siyuan Wang
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  2. Buqing Cao
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  3. Xiang Xie
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  4. Lulu Zhang
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  5. Guosheng Kang
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  6. Jianxun Liu
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Corresponding author

Correspondence to Buqing Cao .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. Zhejiang University City College, Hangzhou, China

    Wei Wei

  4. London South Bank University, London, UK

    Tasos Dagiuklas

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, S., Cao, B., Xie, X., Zhang, L., Kang, G., Liu, J. (2022). An API Recommendation Method Based on Beneficial Interaction. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 460 . Springer, Cham. https://doi.org/10.1007/978-3-031-24383-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-24383-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24382-0

  • Online ISBN: 978-3-031-24383-7

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