# Designing for the Next Click: Bandits for Real-Time Page Layout

arXiv: https://arxiv.org/abs/2608.29850 · HTML: https://mlsanity.com/paper/2608.29850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits](https://mlsanity.com/paper/1402.0555/) (2014)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling](https://mlsanity.com/paper/1810.12027/) (2018)
- [Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://mlsanity.com/paper/2307.15893/) (2023)
- [Page-level Optimization of e-Commerce Item Recommendations](https://mlsanity.com/paper/2108.05891/) (2021)

## Abstract (arXiv)

E-commerce platforms increasingly personalize user experiences through machine learning, yet page layout decisions remain dominated by static rules and manual curation. We present a scalable bandit-based system that optimizes product page layouts in real time while preserving human control over design intent. A contextual bandit model dynamically selects the most effective layout for each session using user, item, and category-level features. The system leverages a LinUCB-based policy to balance exploration and exploitation as it learns from live user interactions. The architecture is designed for seamless integration into large-scale web serving stacks, supporting low-latency inference and continuous model updates. The system was first tested on entry product pages. In online A/B deployments on a major retail platform, our approach achieved positive lifts in session-level performance metrics over a strong heuristic baseline. Our results demonstrate that contextual bandits can effectively optimize visual and structural aspects of product discovery for user engagement, providing a scalable path toward learning-to-design the web.