采用协同过滤技术进行工作流活动推荐

doi:10.3969/j.issn.0372-2112.2017.04.018

电子学报 ›› 2017, Vol. 45 ›› Issue (4): 890-897.DOI: 10.3969/j.issn.0372-2112.2017.04.018

采用协同过滤技术进行工作流活动推荐

陈广智, 何文, 李磊

中山大学数据科学与计算机学院, 广东广州 510006

收稿日期:2015-11-26 修回日期:2016-03-28 出版日期:2017-04-25
- 作者简介:
- 陈广智 男,1981年10月出生,河南太康人.现为中山大学博士研究生,从事工作流推荐、类比推理及迁移学习方面的有关研究.E-mail:chgzhi@mail2.sysu.edu.cn;何文男,1990年8月出生,广东茂名人.于2014年获中山大学软件工程专业学士学位,现于中山大学数据科学与计算机学院攻读硕士学位.研究方向为数据挖掘和推荐系统.
- 基金资助:
- 国家自然科学基金 (No.61300095）; 广东省自然科学基金 (No.S2012040011123）; 广东省教育厅高校优秀青年创新人才培育 (No.2012LYM_-0065）

Workflow Activity Recommendation by Collaborative Filtering

CHEN Guang-zhi, HE Wen, LI Lei

School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong 510006, China

Received:2015-11-26 Revised:2016-03-28 Online:2017-04-25 Published:2017-04-25

摘要/Abstract

摘要：

为解决企事业单位的流程变动问题，利用正常实例和异常实例信息向当前不完整实例推荐下一可能执行的活动.由于每个工作流实例是一个活动名称序列，它们不能直接参与数值运算，需首先将序列中每个活动出现的顺序以数值的形式表示出来，最终将实例库转换成矩阵形式，该矩阵类似于推荐系统中的User-Item矩阵，以便于实例间相似度计算.最后，从实例库中筛选出与当前不完整实例相似性高的完整实例，利用这些实例的信息构造出活动列表，作为推荐结果.实验结果及对比分析表明：我们的活动推荐算法是可行的和有效的.

关键词: 协同过滤, 活动推荐, 推荐系统, 工作流活动, 工作流实例

Abstract:

To address the problem of changes of business processes for an enterprise or organization,we utilize the normal and exceptional instances to recommend the next possible activity for the current incomplete workflow instance.Since every workflow instance is a sequence of activity names,it cannot be calculated numerically.we firstly extract the order of each activity in the sequence as a number value,and then get a matrix which is similar to User-Item matrix in traditional recommendation systems.This matrix can facilitate the calculation of similarity between two workflow instances.Finally,we choose these complete instances which are most similar to the current incomplete instance,construct the activity list as the recommendation result by these instances.Experimental results show that the proposed algorithm is effective and efficient.

Key words: collaborative filtering, activity recommendation, recommendation system, workflow activity, workflow instance

中图分类号:

TP311

陈广智, 何文, 李磊. 采用协同过滤技术进行工作流活动推荐[J]. 电子学报, 2017, 45(4): 890-897.

CHEN Guang-zhi, HE Wen, LI Lei. Workflow Activity Recommendation by Collaborative Filtering[J]. Acta Electronica Sinica, 2017, 45(4): 890-897.

参考文献

[1] MARLON D,WIL M P Aalst,ARTHUR H.Process-Aware Information Systems:Bridging People and Software Through Process Technology[M].New Jersey,NJ,USA:John Wiley & Sons,2005.21-34.
[2] DOMINIC M,JOACHIM H,MARKUS H,et al.IT support for release management processes in the automotive industry[A].Business Process Management[C].Heidelberg:Springer Verlag,2006.368-377.
[3] JAAP G,J M AKKERMANS.Value-based requirements engineering:exploring innovative E-commerce ideas[J].Requirements Engineering,2003,8(2):114-134.
[4] RICHARD L,MANFRED R.IT support for healthcare processes-premises,challenges,perspectives[J].Data & Knowledge Engineering,2007,61(1):39-58.
[5] GIANNIS V,DIMITRIS G,GREGORIS M.Modeling e-government service workflows through recurring patterns[A].Electronic Government[C].Heidelberg:Springer Verlag,2004.483-488.
[6] JOSÉFÉLIX M,CARLOS B,FRANCISCO S.Modeling administrative procedures to improve information to the public[A].Advancing Democracy,Government and Governance[C].Heidelberg:Springer Verlag,2012.155-169.
[7] BELA M,MANFRED R,JOHANNES B.Unleashing the effectiveness of process-oriented information systems:problem analysis,critical success factors,and implications[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2008,38(3):280-291.
[8] JI Z,QING L,KAI X.Flowrecommender:a workflow recommendation technique for process provenance[A].Proceedings of the 8th Australasian Data Mining Conference[C].Melbourne,Australia:ACS,2009.55-61.
[9] AGNES K,THOMAS H,ANDREAS O.Recommendation-based editor for business process modeling[J].Data & Knowledge Engineering,2011,70(6):483-503.
[10] 曹斌,尹建伟,邓永光,等.一种基于近距离最大子图优先的业务流程推荐技术[J].计算机学报,2013,36(2):263-274. CAO Bin,YIN Jian-wei,DENG Yong-guang,et al.A near neighbour and maximal subgraph first based business process recommendation technique[J].Chinese Journal of Computers,2013,36(2):263-274.(in Chinese)
[11] YING L,BIN C,LIDA X,et al.An efficient recommendation method for improving business process modeling[J].IEEE Transactions on Industrial Informatics,2014,10(1):502-513.
[12] KUANG M,LIDAN S,JU F,et al.Competence-based song recommendation:matching songs to one's singing skill[J].IEEE Transactions on Multimedia,2015,17(3):396-408.
[13] AMOS A,AVINATAN H,SARIT K,et al.Movie recommender system for profit maximization[A].Proceedings of the 7th ACM Conference on Recommender Systems[C].New York,NY,USA:ACM,2013.121-128.
[14] RAYMOND J M,LORIENE R.Content-based book recommending using learning for text categorization[A].Proceedings of the 5th ACM Conference on Digital Libraries[C].New York,NY,USA:ACM,2000.195-204.
[15] 黄震华.云环境下top-n推荐算法[J].电子学报,2015,43(1):54-61. HUANG Zhen-hua.Top-n recommendation algorithms for cloud data[J].Acta Electronica Sinica,2015,43(1):54-61.(in Chinese)
[16] 黄震华,张波,方强,等.一种社交网络群组间信息推荐的有效方法[J].电子学报,2015,43(6):1090-1093. HUANG Zhen-hua,ZHANG Bo,FANG Qiang,et al.An efficient algorithm of information recommendation between groups in social networks[J].Acta Electronica Sinica,2015,43(6):1090-1093.(in Chinese)
[17] MUKUND D,GEORGE K.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems,2004,22(1):143-177.
[18] Greg L,BRENT S,JEREMY Y.Amazon.com recommendations:item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80.
[19] BADRUL S,GEORGE K,JOSEPH K,et al.Item-based collaborative filtering recommendation algorithms[A].Proceedings of the 10th International Conference on World Wide Web[C].New York,NY,USA:ACM,2001.285-295.
[20] JOSEPH A K,BRADLEY N M,DAVID M,et al.Grouplens:applying collaborative filtering to usenet news[J].ACM Communications,1997,40(3):77-87.
[21] CHRISTIAN D,GEORGE K.A comprehensive survey of neighborhood-based recommendation methods[A].Recommender Systems Handbook[C].USA:Springer US,2011.207-144.
[22] HELEN S,BARBARA W,BOUDEWIJN D,et al.Supporting flexible processes through recommendations based on history[A].Business Process Management[C].Heidelberg:Springer Verlag,2008.51-66.
[23] RON Z.A programmer's guide to Data Mining:The Ancient Art of the Numerati[OL].http://guidetodatamining.com,2016-03-10.
[24] KOEN V,BART G.Unifying nearest neighbors collaborative filtering[A].Proceedings of the 8th ACM Conference on Recommender Systems[C].New York,USA:ACM,2014.177-184.
[25] JAMES C,YIPING K,WILFRED N.Graphgen:A Graph Synthetic Generator[OL].http://www.cse.ust.hk/graphgen,2016-03-10.
[26] WOOK-SHIN H,JINSOO L,MINH-DUC P,et al.iGraph:a framework for comparisions of disk-based graph indexing techniques[J].Proceedings of the VLDB Endowment,2010,3(1):449-459.
[27] 印桂生,张亚楠,董宇欣,等.基于受限信任关系和概率分解矩阵的推荐[J].电子学报,2014,42(5):904-911. YIN Gui-sheng,ZHANG Ya-nan,DONG Yu-xin,et al.A constrained trust recommendation using probabilistic matrix faccctorization[J].Acta Electronica Sinica,2014,42(5):904-911.(in Chinese)
[28] JINGWEI X,YUAN Y,HANGHANG T,et al.Ice-breaking:mitigating cold-start recommendation problem by rating comparison[A].Proceedings of the 24th International Conference on Artificail Intelligence[C].USA:AAAI Press,2015.3981-3987.
[29] GEORGE K.Evaluation of item-based top-n recommendation algorithms[A].Proceedings of the 10th International Conference on Information and Knowledge Management[C].New York,NY,USA:ACM,2001.247-254.
[30] HORATIU D,MAREK G,NEGAR H,et al.On-demand feature recommendations derived from mining public product descriptions[A].Proceedings of the 33rd International Conference on Software Engineering[C].New York,NY,USA:ACM,2011.181-190.

采用协同过滤技术进行工作流活动推荐

Workflow Activity Recommendation by Collaborative Filtering

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张帅, 高旻, 文俊浩, 熊庆宇, 唐旭. 基于自监督学习的去流行度偏差推荐方法[J]. 电子学报, 2022, 50(10): 2361-2371.
[2]	郑孝遥, 罗永龙, 汪祥舜, 孙丽萍, 陈付龙, 胡桂银, 汪小寒. 基于位置服务的分布式差分隐私推荐方法研究[J]. 电子学报, 2021, 49(1): 99-110.
[3]	郭景峰, 朱晓松, 李爽. 直播电视推荐系统的评分预测算法研究[J]. 电子学报, 2020, 48(9): 1735-1740.
[4]	黄名选, 蒋曹清. 基于项权值排序挖掘的跨语言查询扩展[J]. 电子学报, 2020, 48(3): 568-576.
[5]	任开旭, 王玉龙, 刘同存, 李炜. 融合多维语义表示的概率矩阵分解模型[J]. 电子学报, 2019, 47(9): 1848-1854.
[6]	刘露, 胡封晔, 牛亮, 彭涛. 异质网络中基于节点影响力的相似度度量方法[J]. 电子学报, 2019, 47(9): 1929-1936.
[7]	肖云鹏, 孙华超, 戴天骥, 李茜, 李暾. 一种基于云模型的社交网络推荐系统评分预测方法[J]. 电子学报, 2018, 46(7): 1762-1767.
[8]	曾霖, 卓汉逵, 李磊. 基于智能规划的工作流任务识别算法[J]. 电子学报, 2018, 46(4): 871-877.
[9]	肖云鹏, 刘晏驰, 刘红, 刘媛妮. 一种基于动态角色标识和张量分解的推荐模型[J]. 电子学报, 2018, 46(3): 569-574.
[10]	黄名选, 蒋曹清. 基于完全加权正负关联模式挖掘的越-英跨语言查询译后扩展[J]. 电子学报, 2018, 46(12): 3029-3036.
[11]	鲜征征, 李启良, 黄晓宇, 陆寄远, 李磊. 融合显/隐式信任协同过滤算法的差分隐私保护[J]. 电子学报, 2018, 46(12): 3050-3059.
[12]	俞东进, 陈聪, 吴建华, 陈耀旺. 基于隐式反馈数据的个性化游戏推荐[J]. 电子学报, 2018, 46(11): 2626-2632.
[13]	范利云, 左万利, 王英, 王鑫. 一种基于差分隐私和时序的推荐系统模型研究[J]. 电子学报, 2017, 45(9): 2057-2064.
[14]	俞春花, 刘学军, 李斌, 章玮. 基于上下文相似度和社会网络的移动服务推荐方法[J]. 电子学报, 2017, 45(6): 1530-1536.
[15]	李改, 陈强, 李磊. 基于评分预测与排序预测的协同过滤推荐算法[J]. 电子学报, 2017, 45(12): 3070-3075.