文献类型：期刊文献

英文题名：Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

作者：Chen, Ming[1];Cheng, Junqiang[2];Ma, Guanghua[3];Tian, Liang[4];Li, Xiaohong[3];Shi, Qingmin[3]

第一作者：Chen, Ming

通讯作者：Tian, L[1]

机构：[1]Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450000, Peoples R China;[2]Europe Asia Hitech & Digital Technol Co Ltd, Zhengzhou 450000, Peoples R China;[3]Xinxiang Univ, Key Lab Data Anal & Financial Risk Predict, Xinxiang 458000, Peoples R China;[4]Xinxiang Univ, Inst Comp & Informat Engn, Xinxiang 458000, Peoples R China

第一机构：Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450000, Peoples R China

通讯机构：[1]corresponding author), Xinxiang Univ, Inst Comp & Informat Engn, Xinxiang 458000, Peoples R China.|[1107118]新乡学院计算机与信息工程学院;[11071]新乡学院;

年份：2021

卷号：2021

外文期刊名：SCIENTIFIC PROGRAMMING

收录：;EI(收录号：20214611167449);Scopus(收录号：2-s2.0-85118986104);WOS:【SCI-EXPANDED(收录号:WOS:000729175400003)】；

语种：英文

外文关键词：Classifiers - Recurrent neural networks

摘要：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.