We study the problem of user segmentation: given a set of users and one or
more predefined groups or segments, assign users to their corresponding
segments. As an example, for a segment indicating particular interest in a
certain area of sports or entertainment, the task will be to predict whether
each single user will belong to the segment. However, there may exist numerous
long tail prediction tasks that suffer from data availability and may be of
heterogeneous nature, which make it hard to capture using single off the shelf
model architectures. 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. Following this, we provide an end to end approach that learns to
flexibly attend to best suited heterogeneous experts adaptively, while at the
same time incorporating deep representations of the input concepts that
augments the above experts. Experiments show that SuperCone significantly
outperform state-of-the-art recommendation and ranking algorithms on a wide
range of predicative segment tasks and public structured data learning
benchmarks.

研究用户分割问题，通过 SuperCone 系统中的数字足迹，使用超级学习方法建立预测模型，灵活地 “适应” 异构专家来实现用户分割，并在实验证明此方法的优越性。