用强化学习动态优化电商页面布局，提升用户参与度。
Designing for the Next Click: Bandits for Real-Time Page Layout
- 基于上下文老虎机模型，实时选择最优页面布局。
- 在线测试显示会话级指标显著优于传统规则基线。
- 适合需要自动化设计优化的电商平台从业者。
电商平台越来越多地通过机器学习个性化用户体验，但页面布局决策仍主要依赖静态规则和人工策划。我们提出一种可扩展的基于带权赌博机的系统，可在实时交互中优化产品页面布局，同时保留对设计意图的人工控制。该系统采用上下文老虎机模型，利用用户、商品和类别级别的特征，动态为每个会话选择最有效的布局。基于LinUCB的策略在探索与利用之间取得平衡，持续从真实用户行为中学习。系统架构可无缝集成到大规模网络服务中，支持低延迟推理和持续模型更新。该系统首先在入口商品页面进行测试，在大型零售平台的在线A/B实验中，相比强基准启发式方法，实现了会话级性能指标的正向提升。结果表明，上下文老虎机能有效优化用户参与度相关的视觉与结构特征，为实现‘学习设计网页’提供了可扩展路径。
原文摘要 · Abstract (English)
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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