RLRecommender: A Representation-Learning-Based Recommendation Method for Business Process Modeling

Huaqing Wang, Lijie Wen, Li Lin, Jianmin Wang

Abstract: Most traditional business process recommendation methods cannot deal with complex structures such as interacting loops, and they cannot handle large complex datasets with a great quantity of processes and activities. To address these issues, RLRecommender, a method based on representation learning, is proposed. RLRecommender extracts three kinds of relation sets from the models, both activities and relations between them are projected into a continuous low-dimensional space, and proper activity nodes are recommended by comparing the distances in the space. The experimental results show that our method not only outperforms other baselines on small dataset, but also performs effectively on large dataset.