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

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.

Accepted to The Web Conference 2026 (short paper track), but later withdrawn due to internal prioritization. Subsequently accepted to the Online & Adaptive Recommender Systems Workshop (held in conjunction with the 20th ACM Conference on Recommender Systems, RecSys 2026)

Machine Learning (cs.LG)

arXiv:2608.29850 [cs.LG]

(or arXiv:2608.29850v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.29850 arXiv-issued DOI via DataCite (pending registration)