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Process-aware Recommender systems can provide critical decision supportfunctionality to aid business process execution by recommending what actions totake next. Based on recent advances in the field of deep learning, we present anovel memory-augmented neural network (MANN) based approach for constructing aprocess-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 andusefulness of our approach, we consider three real-world datasets and show thatour approach leads to better performance on several baselines for the task ofsuffix recommendation and next task prediction.

DeepProcess: Supporting business process execution using a MANN-based recommender system