  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.
