TUBE: Embedding Behavior Outcomes for Predicting Success

Given a project plan and the goal, can we predict the plan's success rate? The key challenge is to learn the feature vectors of billions of the plan's components for effective prediction. However, existing methods did not model the behavior outcomes but component proximities. In this work, we define a measurement of behavior outcomes, which forms a test tube-shaped region to represent "success", in a vector space. We propose a novel representation learning method to learn the embeddings of behavior components (including contexts, plans, and goals) by preserving the behavior outcome information. Experiments on real datasets show that our proposed method significantly improves the performance of goal prediction as well as context recommendation over the state-of-the-art.