用生成式方法动态构建电商主页，提升个性化和一致性。
A Cascaded Generative Approach for e-Commerce Recommendations
- 分两步生成：先定页面主题，再为每块生成关键词驱动商品检索。
- 线上实验显示每页浏览量带来2.7%的购物车添加提升。
- 适合需要动态调优、追求页面语义连贯性的电商平台。
大型电商平台的个性化主页通常由多个独立组件构成：各版块的静态主题、商品检索系统和排序模型。该范式虽能优化整体偏好，但结构僵化，难以实现跨版块的个性化与语义统一，不利于动态营销目标的调整。为此，本文提出一种级联生成式商品展示框架，将主页构建分解为两个生成任务：(i) 版块级主题生成，(ii) 每个版块的约束关键词生成以驱动商品检索。采用教师-学生微调策略，在满足生产延迟与成本约束的前提下提升可扩展性。微调模型在消融实验中接近全参数大语言模型性能。进一步引入AI驱动的内容评估与质量过滤框架，支持动态内容的大规模安全部署。生成输出与传统排序模型融合，保留混合基础设施优势。在线实验表明，该框架相比强基线，每页浏览量带来约+2.7%的购物车添加提升。
原文摘要 · Abstract (English)
Personalized storefronts in large e-commerce marketplaces are often assembled from many independent components: static themes per page section ("placement"), retrieval systems to fetch eligible products per placement, and pointwise rankers to order content. While effective in optimizing for aggregate preferences, this paradigm is rigid and can limit personalization and semantic cohesion across the page. This makes it poorly suited to support dynamic objectives and merchandising requirements over time. To address this, we introduce a cascaded merchandising framework that decomposes storefront construction into two generative tasks: (i) placement-level theme generation and (ii) constrained keyword generation per placement to power product retrieval. Teacher-student fine-tuning is leveraged to improve scalability of this framework under production latency and cost constraints. Fine-tuned model ablations are shown to approach closed-weight LLM performance. We further contribute frameworks for AI-driven content evaluation and quality filtering, enabling safe and automated deployment of dynamic content at scale. Generative output is fused with traditional ranking models to preserve hybrid infrastructure. In online experiments, this framework yields an estimated +2.7% lift in cart adds per page view over a strong baseline.
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