DeepProcess: Supporting business process execution using a MANN-based recommender system
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.
Cite
@article{arxiv.1802.00938,
title = {DeepProcess: Supporting business process execution using a MANN-based recommender system},
author = {Asjad Khan and Hung Le and Kien Do and Truyen Tran and Aditya Ghose and Hoa Dam and Renuka Sindhgatta},
journal= {arXiv preprint arXiv:1802.00938},
year = {2021}
}
Comments
Accepted at ICSOC 2021