Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
This addresses the challenge of scalable and automated layout optimization for recommendation systems, though it appears incremental as it applies reinforcement learning to a known problem.
The paper tackled the problem of optimizing geometric layouts for whole-page recommendation by proposing Tile Networks, a reinforcement learning-based neural architecture that arranges items in 2D configurations, achieving superior performance over traditional and deep learning methods on real datasets.
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.