CCeACF: content and complementarity enhanced attentional collaborative filtering for cloud API recommendation.
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| Title: | CCeACF: content and complementarity enhanced attentional collaborative filtering for cloud API recommendation. |
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| Authors: | Chen, Zhen1,2 (AUTHOR) zhenchen@ysu.edu.cn, Chen, Wenhui1 (AUTHOR), Liu, Xiaowei1 (AUTHOR), Zhao, Jing1,2 (AUTHOR) |
| Source: | Journal of Supercomputing. Dec2024, Vol. 80 Issue 18, p26111-26139. 29p. |
| Subjects: | Vector valued functions, Cloud computing, Research personnel, Knowledge transfer, Ecosystems, Application program interfaces |
| Abstract: | Cloud application programming interface (API) is a software intermediary that enables applications to communicate and transfer information to one another in the cloud. As the number of cloud APIs continues to increase, developers are inundated with a plethora of cloud API choices, so researchers have proposed many cloud API recommendation methods. Existing cloud API recommendation methods can be divided into two types: content-based (CB) cloud API recommendation and collaborative filtering-based (CF) cloud API recommendation. CF methods mainly consider the historical information of cloud APIs invoked by mashups. Generally, CF methods have better recommendation performances on head cloud APIs due to more interaction records, and poor recommendation performances on tail cloud APIs. Meanwhile, CB methods can improve the recommendation performances of tail cloud APIs by leveraging the content information of cloud APIs and mashups, but their overall performances are not as good as those of CF methods. Moreover, traditional cloud API recommendation methods ignore the complementarity relationship between mashups and cloud APIs. To address the above issues, this paper first proposes the complementary function vector (CV) based on tag co-occurrence and graph convolutional networks, in order to characterize the complementarity relationship between cloud APIs and mashups. Then we utilize the attention mechanism to systematically integrate CF, CB, and CV methods, and propose a model named Content and Complementarity enhanced Attentional Collaborative Filtering (CCeACF). Finally, the experimental results show that the proposed approach outperforms the state-of-the-art cloud API recommendation methods, can effectively alleviate the long tail problem in the cloud API ecosystem, and is interpretable. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179711750 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CCeACF: content and complementarity enhanced attentional collaborative filtering for cloud API recommendation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Zhen%22">Chen, Zhen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhenchen@ysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Wenhui%22">Chen, Wenhui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaowei%22">Liu, Xiaowei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Jing%22">Zhao, Jing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Dec2024, Vol. 80 Issue 18, p26111-26139. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vector+valued+functions%22">Vector valued functions</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink><br /><searchLink fieldCode="DE" term="%22Application+program+interfaces%22">Application program interfaces</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cloud application programming interface (API) is a software intermediary that enables applications to communicate and transfer information to one another in the cloud. As the number of cloud APIs continues to increase, developers are inundated with a plethora of cloud API choices, so researchers have proposed many cloud API recommendation methods. Existing cloud API recommendation methods can be divided into two types: content-based (CB) cloud API recommendation and collaborative filtering-based (CF) cloud API recommendation. CF methods mainly consider the historical information of cloud APIs invoked by mashups. Generally, CF methods have better recommendation performances on head cloud APIs due to more interaction records, and poor recommendation performances on tail cloud APIs. Meanwhile, CB methods can improve the recommendation performances of tail cloud APIs by leveraging the content information of cloud APIs and mashups, but their overall performances are not as good as those of CF methods. Moreover, traditional cloud API recommendation methods ignore the complementarity relationship between mashups and cloud APIs. To address the above issues, this paper first proposes the complementary function vector (CV) based on tag co-occurrence and graph convolutional networks, in order to characterize the complementarity relationship between cloud APIs and mashups. Then we utilize the attention mechanism to systematically integrate CF, CB, and CV methods, and propose a model named Content and Complementarity enhanced Attentional Collaborative Filtering (CCeACF). Finally, the experimental results show that the proposed approach outperforms the state-of-the-art cloud API recommendation methods, can effectively alleviate the long tail problem in the cloud API ecosystem, and is interpretable. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11227-024-06445-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 26111 Subjects: – SubjectFull: Vector valued functions Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Research personnel Type: general – SubjectFull: Knowledge transfer Type: general – SubjectFull: Ecosystems Type: general – SubjectFull: Application program interfaces Type: general Titles: – TitleFull: CCeACF: content and complementarity enhanced attentional collaborative filtering for cloud API recommendation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Zhen – PersonEntity: Name: NameFull: Chen, Wenhui – PersonEntity: Name: NameFull: Liu, Xiaowei – PersonEntity: Name: NameFull: Zhao, Jing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 80 – Type: issue Value: 18 Titles: – TitleFull: Journal of Supercomputing Type: main |
| ResultId | 1 |
