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
On-line přístup:	Získat plný text
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