Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
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Abstract (arXiv)
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 textitTile 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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 3 Mar 2023.
Builds on
14 of 27 references.
References · 14
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2 more
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2 papers.