Memory-Augmented Neural Networks for Predictive Process Analytics

Muhammad Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Khanh Hoa Dam, Renuka Sindhgatta

Abstract: 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.