面向基于业务流程推荐的协同过滤

Wei Luo, Zhihao Peng, Ansheng Deng, X. Bi
{"title":"面向基于业务流程推荐的协同过滤","authors":"Wei Luo, Zhihao Peng, Ansheng Deng, X. Bi","doi":"10.1504/ijims.2019.10025545","DOIUrl":null,"url":null,"abstract":"Existing process recommendation methods cannot meet the various needs of personalised users. To address this problem, this paper proposed a personalised process recommendation method that is based on user behaviour preference. This method combines traditional process recommendation with user behaviour similarity and mines user behaviour preference according to the historical tracks of processes that were performed by users. In the execution of a process, the execution trace of a behaviour-similar user and executable candidate activities to be recommended that are provided by conventional process recommendation are analysed. Then, activities or recommended activities for the current user are selected to realise the automatic construction of the entire process to meet the personalised needs of users. The experimental results show that the proposed method outperforms other methods in terms of accuracy and efficiency.","PeriodicalId":39293,"journal":{"name":"International Journal of Internet Manufacturing and Services","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Toward business process recommendation-based collaborative filtering\",\"authors\":\"Wei Luo, Zhihao Peng, Ansheng Deng, X. Bi\",\"doi\":\"10.1504/ijims.2019.10025545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Existing process recommendation methods cannot meet the various needs of personalised users. To address this problem, this paper proposed a personalised process recommendation method that is based on user behaviour preference. This method combines traditional process recommendation with user behaviour similarity and mines user behaviour preference according to the historical tracks of processes that were performed by users. In the execution of a process, the execution trace of a behaviour-similar user and executable candidate activities to be recommended that are provided by conventional process recommendation are analysed. Then, activities or recommended activities for the current user are selected to realise the automatic construction of the entire process to meet the personalised needs of users. The experimental results show that the proposed method outperforms other methods in terms of accuracy and efficiency.\",\"PeriodicalId\":39293,\"journal\":{\"name\":\"International Journal of Internet Manufacturing and Services\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Internet Manufacturing and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijims.2019.10025545\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Internet Manufacturing and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijims.2019.10025545","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 1

摘要

现有的流程推荐方法无法满足个性化用户的各种需求。针对这一问题,本文提出了一种基于用户行为偏好的个性化流程推荐方法。该方法将传统的过程推荐与用户行为相似度相结合,根据用户执行过程的历史轨迹挖掘用户行为偏好。在流程的执行过程中,分析了由常规流程推荐提供的行为相似的用户和可执行候选活动的执行轨迹。然后选择当前用户的活动或推荐活动,实现整个流程的自动构建,满足用户的个性化需求。实验结果表明,该方法在精度和效率方面都优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Toward business process recommendation-based collaborative filtering
Existing process recommendation methods cannot meet the various needs of personalised users. To address this problem, this paper proposed a personalised process recommendation method that is based on user behaviour preference. This method combines traditional process recommendation with user behaviour similarity and mines user behaviour preference according to the historical tracks of processes that were performed by users. In the execution of a process, the execution trace of a behaviour-similar user and executable candidate activities to be recommended that are provided by conventional process recommendation are analysed. Then, activities or recommended activities for the current user are selected to realise the automatic construction of the entire process to meet the personalised needs of users. The experimental results show that the proposed method outperforms other methods in terms of accuracy and efficiency.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Internet Manufacturing and Services
International Journal of Internet Manufacturing and Services Engineering-Industrial and Manufacturing Engineering
CiteScore
0.70
自引率
0.00%
发文量
7
期刊最新文献
Cloud Manufacturing Developments: A Review Sustainable Manufacturing of Advanced Mg-Zn-HAp/rGO Hybrid Nanocomposites and Evaluation of Mechanical and Microstructural Properties Analysis of green manufacturing attributes through partial least square structural equation modelling Perspectives of Pilot Testing as a Lean Tool: To conduct a Sustainable Survey in Indian Textile Industry A real-time data acquisition method of industrial production line based on OPC technology
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1