Metadata-based Multi-Attribute Utility Group Recommendation

doi:10.23940/ijpe.20.06.p8.896905

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (6): 896-905.doi: 10.23940/ijpe.20.06.p8.896905

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Metadata-based Multi-Attribute Utility Group Recommendation

Zhao Li^a,b, Xiaofeng Zhang^c, Shuzhen Wan^c, Xiaohong Peng^a,b,*, and Shiyi Xie^a,b

^a College of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, 524088, China;
^b Marine Resources Big Data Center of South China Sea, Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang)Zhanjiang, 524088, China;
^c College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: lgdpxh@126.com
Supported by:
This work was partially supported by the Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (No. ZJW-2019-06, 013S19006-007) and the Program for Scientific Research Start-up Funds of Guangdong Ocean University.

Abstract

Abstract: The performances of current process recommendation need to be improved since they have different degrees of defects. To address this issue, based on metadata, this paper presents a multi-attribute utility group recommendation, which is expected to effectively improve the accuracy of process recommendation. First, a business process description framework (BPDF) is proposed. Then, the similarity between two processes is obtained by calculating the similarity of metadata features. Furthermore, the scenario-oriented group recommendation strategy is developed based on BPDF. The features used in this research are consistent with the key features of the processes in the application. Experimental results show that our approach can improve the effectiveness of business process recommendation.

Key words: business process, multi-attribute utility group, metadata features, business processes recommendation

Zhao Li, Xiaofeng Zhang, Shuzhen Wan, Xiaohong Peng, and Shiyi Xie. Metadata-based Multi-Attribute Utility Group Recommendation [J]. Int J Performability Eng, 2020, 16(6): 896-905.

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