SuperCone: Modeling Heterogeneous Experts with Concept Meta-learning for Unified Predictive Segments System

Understanding users through predicative segments play an essential role for modern enterprises for more efficient and efficient information exchange. For example, by predicting whether a user has particular interest in a particular area of sports or entertainment, we can better serve the user with more relevant and tailored content. However, there exists a large number of long tail prediction tasks that are hard to capture by off the shelf model architectures due to data scarcity and task heterogeneity. In this work, we present SuperCone, our unified predicative segments system that addresses the above challenges. It builds on top of a flat concept representation that summarizes each user's heterogeneous digital footprints, and uniformly models each of the prediction task using an approach called "super learning ", that is, combining prediction models with diverse architectures or learning method that are not compatible with each other or even completely unknown. Following this, we provide end to end deep learning architecture design that flexibly learns to attend to best suited heterogeneous experts while at the same time learns deep representations of the input concepts that augments the above experts by capturing unique signal. Experiments show that SuperCone can outperform state-of-the-art recommendation and ranking algorithms on a wide range of predicative segment tasks, as well as several public structured data learning benchmarks.