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

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 cons...

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Published in:arXiv.org 2021-11
Main Authors: Khan, Asjad, Le, Hung, Do, Kien, Tran, Truyen, Ghose, Aditya, Dam, Hoa, Sindhgatta, Renuka
Format: Article
Language:English
Subjects:
Analytics
Decoding
Machine learning
Mathematical analysis
Neural networks
Predictions
Recommender systems
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creator Khan, Asjad
Le, Hung
Do, Kien
Tran, Truyen
Ghose, Aditya
Dam, Hoa
Sindhgatta, Renuka
description 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.
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subjects Analytics
Decoding
Machine learning
Mathematical analysis
Neural networks
Predictions
Recommender systems
title DeepProcess: Supporting business process execution using a MANN-based recommender system
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