A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention

In current mashup-oriented cloud API recommendation systems, insufficient attention to personalized development requirements remains a common issue, particularly regarding developers' needs for attributes such as functionality similarity and complementarity. This paper proposes a novel approach...

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Vydáno v:	IEEE access Ročník 13; s. 13285 - 13299
Hlavní autoři:	Shen, Limin, Wang, Yuying, Li, Chengyu, Chen, Zhen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Attention mechanism Blogs cloud application programming interface Collaboration Customization Data mining Ecosystems Feature extraction Graph neural networks Graphical representations mashup-oriented Mashups multiple attribute features personalized recommendation Recommender systems Semantics Social networking (online) Tagging
ISSN:	2169-3536, 2169-3536
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Abstract	In current mashup-oriented cloud API recommendation systems, insufficient attention to personalized development requirements remains a common issue, particularly regarding developers' needs for attributes such as functionality similarity and complementarity. This paper proposes a novel approach for personalized cloud API feature representation and recommendation. We construct a graph of the cloud API ecosystem with rich side information and design metapaths to capture and characterize various API features. To fully leverage information from intermediate nodes in the metapaths and emphasize the significance of different instances, we employ a translational distance model and graph neural network techniques to aggregate cloud API feature information. Furthermore, we introduce mashup requirement attention, a mechanism that customizes recommendations based on the specific needs of each mashup project, thereby enhancing the accuracy and personalization of API recommendations. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.
AbstractList	In current mashup-oriented cloud API recommendation systems, insufficient attention to personalized development requirements remains a common issue, particularly regarding developers' needs for attributes such as functionality similarity and complementarity. This paper proposes a novel approach for personalized cloud API feature representation and recommendation. We construct a graph of the cloud API ecosystem with rich side information and design metapaths to capture and characterize various API features. To fully leverage information from intermediate nodes in the metapaths and emphasize the significance of different instances, we employ a translational distance model and graph neural network techniques to aggregate cloud API feature information. Furthermore, we introduce mashup requirement attention, a mechanism that customizes recommendations based on the specific needs of each mashup project, thereby enhancing the accuracy and personalization of API recommendations. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.
Author	Shen, Limin Chen, Zhen Wang, Yuying Li, Chengyu
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SubjectTerms	Accuracy Attention mechanism Blogs cloud application programming interface Collaboration Customization Data mining Ecosystems Feature extraction Graph neural networks Graphical representations mashup-oriented Mashups multiple attribute features personalized recommendation Recommender systems Semantics Social networking (online) Tagging
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Title	A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention
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