Finding optimal configurations in a geometric space is a key challenge in many technological disciplines. Current approaches either rely heavily on human domain expertise and are difficult to scale. In this paper we show it is possible to solve configuration optimization problems for whole-page recommendation using reinforcement learning. The proposed \textit{Tile Networks} is a neural architecture that optimizes 2D geometric configurations by arranging items on proper positions. Empirical results on real dataset demonstrate its superior performance compared to traditional learning to rank approaches and recent deep models.

翻译：在几何空间中寻找最优配置是许多技术领域的核心挑战。现有方法或高度依赖人类领域专业知识，且难以扩展。本文证明，通过强化学习可以解决整页推荐中的配置优化问题。所提出的Tile Networks是一种神经网络架构，通过将项目排列到合适位置来优化二维几何配置。在真实数据集上的实验结果表明，与传统的学习排序方法和近期深度模型相比，该方法具有更优越的性能。