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.

翻译：大型电子商务市场中的个性化店铺页面通常由多个独立组件组装而成：每个页面区域（"位置"）的静态主题、用于获取每个位置合格产品的检索系统以及用于排序内容的逐点排序器。虽然这种方法在优化聚合偏好方面有效，但其范式僵化，可能限制页面内的个性化和语义连贯性，使其难以适应随时间变化的动态目标和营销需求。为解决这一问题，我们引入了一种级联营销框架，将店铺页面构建分解为两个生成任务：(i) 位置级主题生成和(ii) 每个位置受约束的关键词生成以支持产品检索。利用教师-学生微调来改进该框架在生产延迟和成本约束下的可扩展性。微调模型消融实验表明，其性能接近闭源权重大语言模型。我们进一步贡献了用于AI驱动的内容评估和质量过滤的框架，能够安全、自动化地大规模部署动态内容。生成输出与传统排序模型融合以保留混合基础设施。在线实验中，该框架相比强基线，每页面浏览的购物车添加量估计提升+2.7%。