Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

Due to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommend...

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Autores principales:	Ming Chen, Junqiang Cheng, Guanghua Ma, Liang Tian, Xiaohong Li, Qingmin Shi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/ab116ff7aaf049708c29e67772da1cc0
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spelling	oai:doaj.org-article:ab116ff7aaf049708c29e67772da1cc02021-11-08T02:36:27ZService Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes1875-919X10.1155/2021/1013682https://doaj.org/article/ab116ff7aaf049708c29e67772da1cc02021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1013682https://doaj.org/toc/1875-919XDue to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommendation based on recurrent neural network (RNN). When a user selects a service component, the RNN algorithm is exploited to recommend other matched services to the user, aiding the completion of a service composition. The second step is service composition recommendation based on Naive Bayes. When the user completes a service composition, considering the diversity of user interests, the Bayesian classifier is used to model their interests, and other service compositions that satisfy the user interests are recommended to the user. Experiments show that the proposed method can accurately recommend relevant service components and service compositions to users.Ming ChenJunqiang ChengGuanghua MaLiang TianXiaohong LiQingmin ShiHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Ming Chen Junqiang Cheng Guanghua Ma Liang Tian Xiaohong Li Qingmin Shi Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes
description	Due to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommendation based on recurrent neural network (RNN). When a user selects a service component, the RNN algorithm is exploited to recommend other matched services to the user, aiding the completion of a service composition. The second step is service composition recommendation based on Naive Bayes. When the user completes a service composition, considering the diversity of user interests, the Bayesian classifier is used to model their interests, and other service compositions that satisfy the user interests are recommended to the user. Experiments show that the proposed method can accurately recommend relevant service components and service compositions to users.
format	article
author	Ming Chen Junqiang Cheng Guanghua Ma Liang Tian Xiaohong Li Qingmin Shi
author_facet	Ming Chen Junqiang Cheng Guanghua Ma Liang Tian Xiaohong Li Qingmin Shi
author_sort	Ming Chen
title	Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes
title_short	Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes
title_full	Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes
title_fullStr	Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes
title_full_unstemmed	Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes
title_sort	service composition recommendation method based on recurrent neural network and naive bayes
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ab116ff7aaf049708c29e67772da1cc0
work_keys_str_mv	AT mingchen servicecompositionrecommendationmethodbasedonrecurrentneuralnetworkandnaivebayes AT junqiangcheng servicecompositionrecommendationmethodbasedonrecurrentneuralnetworkandnaivebayes AT guanghuama servicecompositionrecommendationmethodbasedonrecurrentneuralnetworkandnaivebayes AT liangtian servicecompositionrecommendationmethodbasedonrecurrentneuralnetworkandnaivebayes AT xiaohongli servicecompositionrecommendationmethodbasedonrecurrentneuralnetworkandnaivebayes AT qingminshi servicecompositionrecommendationmethodbasedonrecurrentneuralnetworkandnaivebayes
_version_	1718443126537846784

Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

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