Personalizing the user experience with high-quality recommendations based on user activities is vital for e-commerce platforms. This is particularly important in scenarios where the user's intent is not explicit, such as on the homepage. Recently, personalized embedding-based systems have significantly improved the quality of recommendations and search results in the e-commerce domain. However, most of these works focus on enhancing the retrieval stage. In this paper, we demonstrate that features produced by retrieval-focused deep learning models are sub-optimal for ranking stage in e-commerce recommendations. To address this issue, we propose a two-stage training process that fine-tunes two-tower models to achieve optimal ranking performance. We provide a detailed description of our transformer-based two-tower model architecture, which is specifically designed for personalization in e-commerce. Additionally, we introduce a novel technique for debiasing context in offline models and report significant improvements in ranking performance when using web-search queries for e-commerce recommendations. Our model has been successfully deployed at Yandex and has delivered strong performance in online A/B testing.

翻译：基于用户行为的高质量推荐以个性化用户体验，对电商平台至关重要。这在用户意图不明确的场景（如主页）中尤为关键。近年来，基于个性化嵌入的系统显著提升了电商领域推荐与搜索结果的质量。然而，多数此类研究聚焦于召回阶段优化。本文证明，以召回为导向的深度学习模型产生的特征在电商推荐排序阶段存在次优表现。为解决此问题，我们提出两阶段训练流程，通过微调双塔模型实现最优排序性能。我们详细阐述了专为电商个性化设计的Transformer双塔模型架构，并创新性地提出离线模型上下文去偏技术。实验表明，将网页搜索查询用于电商推荐可显著提升排序性能。该模型已在Yandex成功部署，并在线上A/B测试中展现出卓越表现。