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

• URL: http://arxiv.org/abs/2608.29850v1
• Date: Sun, 30 Aug 2026 15:28:15 GMT
• Title: Designing for the Next Click: Bandits for Real-Time Page Layout
• Abstract summary: We present a scalable bandit-based system that optimize product page layouts in real time.<n>A contextual bandit model dynamically selects the most effective layout for each session using user, item, and category-level features.<n>The architecture is designed for seamless integration into large-scale web serving stacks.
• Score: 0.6742864446722399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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