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MSRDL: Deep learning framework for service recommendation in mashup creation
In recent years, service-oriented computing technology has developed rapidly. The growing number of services increases the choice burden of software developers when developing service-based systems, such as mashups or applications. How to recommend appropriate services for developers to create mashu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175400/ https://www.ncbi.nlm.nih.gov/pubmed/37169772 http://dx.doi.org/10.1038/s41598-023-32814-y |
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author | Yu, Ting Liu, Hailin Zhang, Lihua Liu, Hongbing |
author_facet | Yu, Ting Liu, Hailin Zhang, Lihua Liu, Hongbing |
author_sort | Yu, Ting |
collection | PubMed |
description | In recent years, service-oriented computing technology has developed rapidly. The growing number of services increases the choice burden of software developers when developing service-based systems, such as mashups or applications. How to recommend appropriate services for developers to create mashups has become a basic problem in service-oriented recommendation systems. To solve this problem, people have proposed various methods to recommend services to match the requirements of the new mashups and achieved great success. However, there are also some challenges in feature utilization and text requirement understanding. Therefore, we propose a Mashup-oriented Service Recommendation framework based on Deep Learning, called MSRDL. A content component was designed in MSRDL to generate the representation of mashups and services. Besides, an interaction component was created in MSRDL to model the invocation records between mashups and services. The output features of the two parts are further integrated into MLP to obtain the service recommendation lists. Experimental results on ProgrammableWeb datasets show that our method is superior to the state-of-the-art methods. |
format | Online Article Text |
id | pubmed-10175400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101754002023-05-13 MSRDL: Deep learning framework for service recommendation in mashup creation Yu, Ting Liu, Hailin Zhang, Lihua Liu, Hongbing Sci Rep Article In recent years, service-oriented computing technology has developed rapidly. The growing number of services increases the choice burden of software developers when developing service-based systems, such as mashups or applications. How to recommend appropriate services for developers to create mashups has become a basic problem in service-oriented recommendation systems. To solve this problem, people have proposed various methods to recommend services to match the requirements of the new mashups and achieved great success. However, there are also some challenges in feature utilization and text requirement understanding. Therefore, we propose a Mashup-oriented Service Recommendation framework based on Deep Learning, called MSRDL. A content component was designed in MSRDL to generate the representation of mashups and services. Besides, an interaction component was created in MSRDL to model the invocation records between mashups and services. The output features of the two parts are further integrated into MLP to obtain the service recommendation lists. Experimental results on ProgrammableWeb datasets show that our method is superior to the state-of-the-art methods. Nature Publishing Group UK 2023-05-11 /pmc/articles/PMC10175400/ /pubmed/37169772 http://dx.doi.org/10.1038/s41598-023-32814-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yu, Ting Liu, Hailin Zhang, Lihua Liu, Hongbing MSRDL: Deep learning framework for service recommendation in mashup creation |
title | MSRDL: Deep learning framework for service recommendation in mashup creation |
title_full | MSRDL: Deep learning framework for service recommendation in mashup creation |
title_fullStr | MSRDL: Deep learning framework for service recommendation in mashup creation |
title_full_unstemmed | MSRDL: Deep learning framework for service recommendation in mashup creation |
title_short | MSRDL: Deep learning framework for service recommendation in mashup creation |
title_sort | msrdl: deep learning framework for service recommendation in mashup creation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175400/ https://www.ncbi.nlm.nih.gov/pubmed/37169772 http://dx.doi.org/10.1038/s41598-023-32814-y |
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