  Accurate understanding of users in terms of predicative segments play an
essential role in the day to day operation of modern internet enterprises.
Nevertheless, there are significant challenges that limit the quality of data,
especially on long tail predictive tasks. In this work, we present MetaCon, our
unified predicative segments system with scalable, trillion concepts meta
learning that addresses these challenges. It builds on top of a flat concept
representation that summarizes entities' heterogeneous digital footprint,
jointly considers the entire spectrum of predicative tasks as a single learning
task, and leverages principled meta learning approach with efficient first
order meta-optimization procedure under a provable performance guarantee in
order to solve the learning task. Experiments on both proprietary production
datasets and public structured learning tasks demonstrate that MetaCon can lead
to substantial improvements over state of the art recommendation and ranking
approaches.
