🤖 AI Summary
This work addresses the limitations of traditional e-commerce recommendation systems, which suffer from fragmented components that hinder page-level personalization, semantic coherence, and flexible adaptation to dynamic operational objectives. To overcome these challenges, the authors propose a cascaded generative product display framework that decomposes homepage construction into two stages: layout-slot theme generation and constrained keyword generation. The approach integrates teacher-student distillation for computational efficiency and incorporates conventional ranking models to preserve the benefits of hybrid architectures. This design enables end-to-end dynamic content generation coupled with AI-driven quality filtering, achieving a balance between personalization, semantic consistency, and deployment safety. Online experiments demonstrate a 2.7% increase in add-to-cart actions per page view compared to strong baselines, with the fine-tuned model attaining performance comparable to that of closed-source large language models.
📝 Abstract
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.