Tile Networks：学习最优几何布局的全页推荐
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
摘要：
在几何空间中寻找最佳配置是许多技术学科中的关键挑战。目前的方法要么严重依赖于人类领域的专业知识，难以扩展。在本文中，我们表明，它是可能的解决配置优化问题的整页推荐使用强化学习。所提出的\textit{Tile Networks}是一种神经结构，通过在适当的位置上排列项目来优化2D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最近的深度模型相比，它具有更好的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
