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

2026-08-30

AI Summary

本文提出一种基于上下文强盗算法的系统，实现实时优化电商产品页面布局，通过用户、商品和类别特征动态选择最佳布局，提高用户参与度。

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.

Problem

• E-commerce
• Page Layout
• Personalization
• Contextual Bandits

Innovation

• contextual bandit
• real-time optimization
• page layout
• LinUCB
• personalization