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 ap...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10767143/ |
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| author | Limin Shen Yuying Wang Chengyu Li Zhen Chen |
| author_facet | Limin Shen Yuying Wang Chengyu Li Zhen Chen |
| author_sort | Limin Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-28d4e4cfa8c14a19943904390a62425a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-28d4e4cfa8c14a19943904390a62425a2025-01-28T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113132851329910.1109/ACCESS.2024.350594310767143A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement AttentionLimin Shen0Yuying Wang1https://orcid.org/0000-0002-1520-5557Chengyu Li2Zhen Chen3https://orcid.org/0000-0003-4424-7315College of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaIn 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.https://ieeexplore.ieee.org/document/10767143/Attention mechanismcloud application programming interfacemashup-orientedmultiple attribute featurespersonalized recommendation |
| spellingShingle | Limin Shen Yuying Wang Chengyu Li Zhen Chen A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention IEEE Access Attention mechanism cloud application programming interface mashup-oriented multiple attribute features personalized recommendation |
| title | A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention |
| title_full | A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention |
| title_fullStr | A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention |
| title_full_unstemmed | A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention |
| title_short | A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention |
| title_sort | cloud api personalized recommendation method based on multiple attribute features and mashup requirement attention |
| topic | Attention mechanism cloud application programming interface mashup-oriented multiple attribute features personalized recommendation |
| url | https://ieeexplore.ieee.org/document/10767143/ |
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