Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation

• URL: http://arxiv.org/abs/2303.01671v1
• Date: Fri, 3 Mar 2023 02:18:55 GMT
• Title: Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
• Authors: Shuai Xiao, Zaifan Jiang, Shuang Yang
• Abstract summary: We show it is possible to solve configuration optimization problems for whole-page recommendation using reinforcement learning. The proposed textitTile Networks is a neural architecture that optimize 2D geometric configurations by arranging items on proper positions.
• Score: 14.951408879079272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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