Workflow Recommendation Based on Graph Embedding

Xiaoming Yu, Wenjun Wu, Xingchuang Liao

Abstract: In order to complete design and modeling of workflow more effectively, enterprises urgently need efficient workflow recommendation technology. At present, traditional recommendation algorithms based on process structure are widely used, yet tedious modeling operations and poor recommendation accuracy are noteworthy issues. To address the above problems, based on complex workflow relationships, we utilize graph embedding in workflow recommendation to provide convenience for business process operators. In this paper, we propose a Workflow Embedding Recommendation(namely WFER) method, which can deal with the adjacency matrix of complex process to obtain more detailed feature representation, so as to calculate the similarity accurately. Therefore, we implement efficient recommendation based on workflow semantics. Moreover, this recommendation tool is suitable for both transactional workflows and scientific workflows. Finally, based on real datasets and generated datasets, we carry out experiments to compare our method with other traditional algorithms and experimental results show its effectiveness and efficiency in practice.

External IDs:dblp:conf/services/Yu0L20