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

TL;DR

DeepProcess introduces a novel MANN-based recommender system with a specialized architecture to improve process execution support, demonstrating superior performance on real-world datasets for next task prediction.

Contribution

The paper presents Write-Protected Dual Controller MANN, a new neural network architecture specifically designed for process-aware recommendation tasks.

Findings

Outperforms baseline models in suffix recommendation.

Effective on three real-world datasets.

Improves next task prediction accuracy.

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.