An increasingly important building block of large scale machine learning
systems is based on returning slates; an ordered lists of items given a query.
Applications of this technology include: search, information retrieval and
recommender systems. When the action space is large, decision systems are
restricted to a particular structure to complete online queries quickly. This
paper addresses the optimization of these large scale decision systems given an
arbitrary reward function. We cast this learning problem in a policy
optimization framework and propose a new class of policies, born from a novel
relaxation of decision functions. This results in a simple, yet efficient
learning algorithm that scales to massive action spaces. We compare our method
to the commonly adopted Plackett-Luce policy class and demonstrate the
effectiveness of our approach on problems with action space sizes in the order
of millions.

大规模机器学习系统的一个关键组成部分是返回选项列表；这篇论文在给定任意奖励函数的情况下，通过在决策函数上应用一种新的松弛技术，提出了一种简单而高效的学习算法来优化这些大规模决策系统，能够扩展到庞大的动作空间规模，相比常用的 Plackett-Luce 策略类，证明了该方法的有效性。