Personalizing user experience with high-quality recommendations based on user activity 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 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, serves millions of users daily, and has delivered strong performance in online A/B testing.

翻译：基于用户活动提供高质量个性化推荐以优化用户体验，对于电商平台至关重要。在用户意图不明确的情境（如首页推荐）中尤为重要。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的质量，但多数研究聚焦于检索阶段优化。本文证明，用于检索的深度学习模型生成的特征在电商推荐排序阶段表现欠佳。为解决该问题，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。本文详细描述了专为电商个性化设计的基于Transformer的双塔模型架构，并引入离线模型上下文去偏新技术。实验表明，将网络搜索查询用于电商推荐时，排序性能获得显著提升。该模型已在Yandex成功部署，每日服务数百万用户，在线A/B测试中展现出卓越性能。