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.
翻译:我们研究用户分化问题:根据一组用户以及一个或多个预设组或部分,将用户分配到相应的部分。举例来说,对于显示对体育或娱乐某一领域特别感兴趣的部分,任务将是预测每个用户是否属于该部分;然而,可能存在许多长尾预测任务,这些任务受数据提供的影响,而且可能是多种多样的,因此很难利用架子模型结构外的单个专家来捕捉。在这项工作中,我们介绍了我们用来应对上述挑战的统一预设区块系统超级Cone。它以一个平坦的概念代表制为顶端,该代表制以总结每个用户的不同数字足迹,以及每个预测任务的统一模型,使用一个称为 " 超级学习 " 的方法,即将预测模型与不同结构或相互不兼容的学习方法结合起来。此后,我们提出一种结束方法,即学会灵活地与最适合的多元专家打交道,同时纳入能够增加上述专家的投入概念的深度表述。实验显示,SupCone 明显超越了公共分级前阶段级排序和结构排序。