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Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
- Publication Year :
- 2023
-
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.<br />Comment: Published at Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022
- Subjects :
- Computer Science - Artificial Intelligence
Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2303.01671
- Document Type :
- Working Paper