Singular value decomposition‐based behavior‐aware cloud service application programming interfaces recommendation for large‐scale software cloud directory platforms
Summary With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large‐scale software cloud directory p...
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| Vydáno v: | Concurrency and computation Ročník 34; číslo 21 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken
Wiley Subscription Services, Inc
25.09.2022
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| ISSN: | 1532-0626, 1532-0634 |
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| Abstract | Summary
With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large‐scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform. Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self‐description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD‐APIR, a singular value decomposition (SVD)‐based behavior‐aware cloud service APIs recommendation approach for large‐scale software cloud directory platforms. In SVD‐APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS‐Dream dataset demonstrate the effectiveness and efficiency of the proposed approach. |
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| AbstractList | With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large‐scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform. Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self‐description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD‐APIR, a singular value decomposition (SVD)‐based behavior‐aware cloud service APIs recommendation approach for large‐scale software cloud directory platforms. In SVD‐APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS‐Dream dataset demonstrate the effectiveness and efficiency of the proposed approach. Summary With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large‐scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform. Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self‐description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD‐APIR, a singular value decomposition (SVD)‐based behavior‐aware cloud service APIs recommendation approach for large‐scale software cloud directory platforms. In SVD‐APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS‐Dream dataset demonstrate the effectiveness and efficiency of the proposed approach. |
| Author | Wang, Lei Zhang, Yunqiu Chen, Shuhan Yu, Qi Ding, Junyao Zheng, Xubin |
| Author_xml | – sequence: 1 givenname: Lei orcidid: 0000-0002-1511-7266 surname: Wang fullname: Wang, Lei email: leiwangchn@163.com organization: Nanjing Forestry University – sequence: 2 givenname: Yunqiu surname: Zhang fullname: Zhang, Yunqiu organization: Nanjing Forestry University – sequence: 3 givenname: Xubin surname: Zheng fullname: Zheng, Xubin organization: The Chinese University of Hong Kong – sequence: 4 givenname: Qi surname: Yu fullname: Yu, Qi organization: Rochester Institute of Technology – sequence: 5 givenname: Shuhan surname: Chen fullname: Chen, Shuhan organization: Nanjing Forestry University – sequence: 6 givenname: Junyao surname: Ding fullname: Ding, Junyao organization: Nanjing Forestry University |
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| Cites_doi | 10.1109/SCC.2014.47 10.1109/TSC.2015.2480393 10.1109/TSC.2010.52 10.1007/s10115-013-0723-x 10.1016/j.dss.2017.12.012 10.1016/j.eswa.2016.01.010 10.1109/TPDS.2013.246 10.1109/TSC.2016.2636285 10.1109/TSC.2020.3046360 10.1007/s10489-021-02553-6 10.1017/CBO9781139924801 10.1109/TPDS.2018.2885746 10.1109/TSC.2016.2592903 10.1016/j.jss.2020.110902 10.1109/TCSS.2019.2906925 10.1016/j.future.2020.08.014 10.1145/2983528 10.1109/TPDS.2019.2917900 10.1002/cpe.5450 10.1016/j.knosys.2016.02.016 10.1109/TPDS.2018.2877363 10.1109/TSC.2016.2584058 10.1109/CCGrid.2015.144 10.1109/JSAC.2017.2760458 10.1016/j.knosys.2018.02.014 10.1002/cpe.5462 10.1109/ICWS.2015.60 10.1109/TSE.2016.2615615 10.1145/2911451.2911538 10.1109/TSC.2016.2602203 10.1016/j.eswa.2019.01.025 10.1016/j.future.2019.02.063 10.1002/cpe.5195 10.1016/j.jpdc.2018.04.002 10.1016/j.ins.2018.11.030 10.1016/j.dss.2016.09.001 10.1016/j.knosys.2016.09.033 10.1109/TSC.2015.2433251 10.1109/TNSM.2017.2693324 |
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| Notes | Funding information Humanity and Social Science Youth Fund of Ministry of Education of China, Grant/Award Number: 18YJCZH170; Innovative Training Program for College Students in Jiangsu Province, Grant/Award Number: 202110298077Y; Six Talent Peaks Project in Jiangsu Province in 2019, Grant/Award Number: RJFW‐029 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in... With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the... |
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| Title | Singular value decomposition‐based behavior‐aware cloud service application programming interfaces recommendation for large‐scale software cloud directory platforms |
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