DeepProcess: Supporting business process execution using a MANN-based recommender system
This work addresses the need for decision support in business process execution, though it appears incremental as it builds on existing deep learning methods for process-aware recommender systems.
The authors tackled the problem of recommending next actions in business processes by proposing a novel memory-augmented neural network (MANN) architecture called DCw-MANN, which demonstrated better performance on suffix recommendation and next task prediction across three real-world datasets compared to several baselines.
Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing a process-aware recommender system. We propose a novel network architecture, namely Write-Protected Dual Controller Memory-Augmented Neural Network (DCw-MANN), for building prescriptive models. To evaluate the feasibility and usefulness of our approach, we consider three real-world datasets and show that our approach leads to better performance on several baselines for the task of suffix recommendation and next task prediction.