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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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 13285 - 13299
Main Authors: Shen, Limin, Wang, Yuying, Li, Chengyu, Chen, Zhen
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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)
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ISSN:2169-3536, 2169-3536
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3505943