Tile Networks: Learning Optimal Geometric Layout for Whole-Page Recommendation
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 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.
Cite
Text
Xiao et al. " Tile Networks: Learning Optimal Geometric Layout for Whole-Page Recommendation ." Artificial Intelligence and Statistics, 2022.Markdown
[Xiao et al. " Tile Networks: Learning Optimal Geometric Layout for Whole-Page Recommendation ." Artificial Intelligence and Statistics, 2022.](https://mlanthology.org/aistats/2022/xiao2022aistats-tile/)BibTeX
@inproceedings{xiao2022aistats-tile,
title = {{ Tile Networks: Learning Optimal Geometric Layout for Whole-Page Recommendation }},
author = {Xiao, Shuai and Jiang, Zaifan and Yang, Shuang},
booktitle = {Artificial Intelligence and Statistics},
year = {2022},
pages = {8360-8369},
volume = {151},
url = {https://mlanthology.org/aistats/2022/xiao2022aistats-tile/}
}