Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of the prediction being conformant or meeting the goal. Hence, we propose a goal-oriented next best activity recommendation. Our proposed framework uses a deep learning model to predict the next best activity and an estimated value of a goal given the activity. A reinforcement learning method explores the sequence of activities based on the estimates likely to meet one or more goals. We further address a real-world problem of multiple goals by introducing an additional reward function to balance the outcome of a recommended activity and satisfy the goal. We demonstrate the effectiveness of the proposed method on four real-world datasets with different characteristics. The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques.
翻译:为进行中的案件建议一系列活动要求建议符合基本的业务流程,并达到完成时间或进程结果的绩效目标。关于下一个活动预测的现有工作可以预测未来活动,但不能保证预测是否符合或达到目标。因此,我们提出一个面向目标的下一个最佳活动建议。我们提议的框架使用一个深层次学习模型来预测下一个最佳活动,并估计活动目标的价值。一个强化学习方法根据可能达到一个或多个目标的估计,探索活动的顺序。我们进一步解决一个多重目标的现实世界问题,方法是增加一个奖励功能,平衡所建议活动的结果,并达到目标。我们展示了四个具有不同特点的真实世界数据集的拟议方法的有效性。结果显示,我们拟议方法的建议在目标满意度和与现有最先进的下一个最佳活动建议技术相比,在目标满意度和一致性方面都出问题。