一种适用于业务流程定制环境的服务推荐方案
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摘要
在个性化业务流程定制环境中,为提高用户的定制效率引入推荐技术,但现有推荐技术存在诸多问题,如新用户、推荐结果缺乏多样性与推荐质量偏低等。为充分发挥推荐技术的优势并解决上述问题,提出一种适用于业务流程定制环境的服务推荐方案,该方案首先利用聚类算法来解决新用户问题,再通过优化协同过滤来提高推荐结果的多样性,然后构建内容过滤筛除"伪邻居"来提高推荐质量,最后将获得的候选服务推荐给用户来提高定制效率。通过实验研究证实,所提方案既能解决新用户问题,又能改善推荐结果的多样性与推荐质量,还能提高用户的定制效率。
In the personalized business process customization environment,recommendation technology is introduced to improve the user's customization efficiency,but the existing recommendation technology has many problems,such as the new users,the lack of diversity of recommendation results and low recommendation quality.To give full play to the advantages of recommended technologies and solve the above problems,a service recommendation scheme suitable for business process customization environment was proposed.In this scheme,the clustering algorithm was used to solve new user problems,then the collaborative filtering was optimized to improve the diversity of recommendation results.The content filtering was built to screen out "pseudo-neighbors" for improving the recommendation quality,and the candidate services were recommended to the user to improve customization efficiency.The experimental research proved that the scheme proposed could solve the problem of new users,improve the diversity of recommendation results and recommendation quality,and enhance the customization efficiency of the users.
In the personalized business process customization environment,recommendation technology is introduced to improve the user's customization efficiency,but the existing recommendation technology has many problems,such as the new users,the lack of diversity of recommendation results and low recommendation quality.To give full play to the advantages of recommended technologies and solve the above problems,a service recommendation scheme suitable for business process customization environment was proposed.In this scheme,the clustering algorithm was used to solve new user problems,then the collaborative filtering was optimized to improve the diversity of recommendation results.The content filtering was built to screen out "pseudo-neighbors" for improving the recommendation quality,and the candidate services were recommended to the user to improve customization efficiency.The experimental research proved that the scheme proposed could solve the problem of new users,improve the diversity of recommendation results and recommendation quality,and enhance the customization efficiency of the users.
引文
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[23]JIANG Xinjing,QI Xiaogang,LIU Lifang.Research on the recommendation method of personalized information[J].CAAI Transactions on Intelligent Systems,2018,13(2):189-195(in Chinese).[姜信景,齐小刚,刘立芳.个性化信息推荐方法研究[J].智能系统学报,2018,13(2):189-195.]
[24]JANNACH D,ZANKER M,FELFERNIG A,et al.Recommendation system[M].JIANG Fan,transl.Beijing:People's Posts and Telecommunications Press,2013:9-86(in Chinese).[JANNACH D,ZANKER M,FELFERNIG A,等.推荐系统[M].蒋凡,译.北京:人民邮电出版社,2013:9-86.]
[25]Southwest Jiaotong University.Auto parts multi-industry chain collaborative cloud service platform[EB/OL].[2018-02-06].http://autosaas.cn(125.69.67.34:10000),2012(in Chinese).[西南交通大学.汽车零部件多产业链协同云服务平台[EB/OL].[2018-02-06].http://autosaas.cn(125.69.67.34:10000),2012.]
[2]LI Bingyong,HAN Min,SUN Linfu,et al.Multi-layer network collaborative control system of cloud services platform[J].Computer Integrated Manufacturing Systems,2015,21(5):1382-1388(in Chinese).[李斌勇,韩敏,孙林夫,等.云服务平台多层网络协同控制模型[J].计算机集成制造系统,2015,21(5):1382-1388.].
[3]YU Yang,SUN Linfu,MA Yahua.Access control model for attribute-based cloud manufacturing collaboration platform[J].Computer Integrated Manufacturing Systems,2017,23(1):196-202(in Chinese).[余洋,孙林夫,马亚花.基于属性的云制造协同平台访问控制模型[J].计算机集成制造系统,2017,23(1):196-202.]
[4]CHEN Jing,SUN Linfu.Solutions of data security for industrial chain collaboration public service platform based on SaaS[J].Computer Integrated Manufacturing Systems,2011,17(6):1317-1324(in Chinese).[陈静,孙林夫.基于SaaS的产业链协作公共服务平台数据安全解决方案[J].计算机集成制造系统,2011,17(6):1317-1324.]
[5]YU Yang,SUN Linfu,WANG Shuying,et al.Data security model for industrial chain collaborative SaaS platform[J].Computer Integrated Manufacturing Systems,2016,22(12):2911-2919(in Chinese).[余洋,孙林夫,王淑营,等.面向产业链协同SaaS平台的数据安全模型[J].计算机集成制造系统,2016,22(12):2911-2919.]
[6]YU Y,SUN L,WU Q,et al.Fuzzyquery model of the business resources based on cloud service platform[C]//Proceedings of the 2nd International Conference on Computer Science and Application Engineering.New York,N.Y.,USA:ACM,2018.DOI:http://dx.doi.org/10.1145/3207677.3278082.
[7]YU Yang,SUN Linfu,MA Yahua.Multi-tenant form customization technology for collaborative cloud service platform in industrial chain[J].Computer Integrated Manufacturing Systems,2016,22(9):2235-2244(in Chinese).[余洋,孙林夫,马亚花.面向产业链协同云服务平台的多租户表单定制技术[J].计算机集成制造系统,2016,22(9):2235-2244.]
[8]SHI Yuliang,LUAN Shuai,LI Qingzhong,et al.TLA based customization and verification mechanism of business process for SaaS[J].Chinese Journal of Computers,2010,33(11):2055-2067(in Chinese).[史玉良,栾帅,李庆忠,等.基于TLA的SaaS业务流程定制及验证机制研究[J].计算机学报,2010,33(11):2055-2067.]
[9]HUANG Liwei,JIANG Bitao,LYU Shouye,et al.Survey on deep learning based recommender systems[J].Chinese Journal of Computers,2018,41(7):1619-1647(in Chinese).[黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):1619-1647.]
[10]LIU Pingfeng,NIE Guihua,CHEN Donglin.A survey:electronic commerce recommender systems[J].Journal of Information,2007,41(7):46-49(in Chinese).[刘平峰,聂规划,陈冬林.电子商务推荐系统研究综述[J].情报杂志,2007,41(7):46-49.]
[11]ZENG Chun,XING Chunxiao,ZHOU Lizhu.A personalized search algorithm by using content-based filtering[J].Journal of Software,2003,14(5):999-1004(in Chinese).[曾春,邢春晓,周立柱.基于内容过滤的个性化搜索算法[J].软件学报,2003,14(5):999-1004.]
[12]GOOSSEN F,IJNTEMA W,FRASINCAR F,et al.News ersonalization using the CF-IDF semantic recommender[C]//Proceedings of the 2011International Conference on Web Intelligence,Mining and Semantics.New York,N.Y.,USA:ACM,2011:10-21.
[13]RAO Junyang,JIA Aixia,FENG Yansong,et al.Ontologybased news personalized recommendation[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2014,50(1):2-7(in Chinese).[饶俊阳,贾爱霞,冯岩松,等.基于本体结构的新闻个性化推荐[J].北京大学学报:自然科学版,2014,50(1):2-7.]
[14]ADNAN M N M,CHOWDURY M R,TAZ I,et al.Content based news recommendation system based on fuzzy logic[C]//Proceedings of the 2014International Conference on Informatics,Electronic&Vision.Washington,D.C.,USA:IEEE Computer Society,2014:1-6.
[15]MI Ruanjianchi,ZHAN Hongfei,YU Junhe.Research on the knowledge context modeling method for enterprise knowledge recommendation[J].Information Studies:Theory&Application,2016,39(4):78-83(in Chinese).[密阮建驰,战洪飞,余军合.面向企业知识推荐的知识情景建模方法研究[J].情报理论与实践2016,39(4):78-83.]
[16]DURAND G,BELACEL N,LAPLANTE F.Graph theory based model for learning path recommendation[J].Information Sciences,2013,251(1):10-21.
[17]LIU Haitao,ZHAO Weidong.Process-oriented knowledge recommendation by mining knowledge patterns[J].Computer Integrated Manufacturing Systems,2017,23(2):396-403(in Chinese).[刘海涛,赵卫东.基于知识模式挖掘的流程知识推荐系统[J].计算机集成制造系统,2017,23(2):396-403.]
[18]RONG Huigui,HUO Shengxu,HU Chunhua,et al.User similarity-based collaborative filtering recommendation algorithm[J].Journal on Communications,2014,35(2):16-24(in Chinese).[荣辉桂,火生旭,胡春华,等.基于用户相似度的协同过滤推荐算法[J].通信学报,2014,35(2):16-24.]
[19]XU Zhihong,WANG Baoying.Collaborative filtering algorithm based on item complex similarity[J].Application Research of Computers,2014,31(2):398-400(in Chinese).[许智宏,王宝莹.基于项目综合相似度的协同过滤算法[J].计算机应用研究,2014,31(2):398-400.]
[20]WANG Haiyan,YANG Wenbin,WANG Suichang,et al.AService recommendation method based on trust worthy community[J].Chinese Journal of Computers,2014,37(2):301-311(in Chinese).[王海艳,杨文彬,王随昌,等.基于可信联盟的服务推荐方法[J].计算机学报,2014,37(2):301-311.]
[21]WANG Mingjia,HAN Jingti,HAN Songqiao.Collaborative filtering algorithm based on fuzzy clustering[J].Computer Engineering,2012,38(24):50-52(in Chinese).[王明佳,韩景倜,韩松乔.基于模糊聚类的协同过滤算法[J].计算机工程,2012,38(24):50-52.]
[22]YANG Wu,TANG Rui,LU Ling.News recommendation method by fusion of content-based recommendation and collaborative filtering[J].Journal of Computer Applications,2016,36(2):414-418(in Chinese).[杨武,唐瑞,卢玲.基于内容的推荐与协同过滤融合的新闻推荐方法[J].计算机应用,2016,36(2):414-418.]
[23]JIANG Xinjing,QI Xiaogang,LIU Lifang.Research on the recommendation method of personalized information[J].CAAI Transactions on Intelligent Systems,2018,13(2):189-195(in Chinese).[姜信景,齐小刚,刘立芳.个性化信息推荐方法研究[J].智能系统学报,2018,13(2):189-195.]
[24]JANNACH D,ZANKER M,FELFERNIG A,et al.Recommendation system[M].JIANG Fan,transl.Beijing:People's Posts and Telecommunications Press,2013:9-86(in Chinese).[JANNACH D,ZANKER M,FELFERNIG A,等.推荐系统[M].蒋凡,译.北京:人民邮电出版社,2013:9-86.]
[25]Southwest Jiaotong University.Auto parts multi-industry chain collaborative cloud service platform[EB/OL].[2018-02-06].http://autosaas.cn(125.69.67.34:10000),2012(in Chinese).[西南交通大学.汽车零部件多产业链协同云服务平台[EB/OL].[2018-02-06].http://autosaas.cn(125.69.67.34:10000),2012.]