Personalized Transformer-based Ranking for e-Commerce at Yandex
Kirill Khrylchenko, Alexander Fritzler

TL;DR
This paper presents a transformer-based two-tower model for personalized e-commerce recommendations, emphasizing the importance of the ranking stage, and demonstrates significant offline and online performance improvements at Yandex.
Contribution
The paper introduces a novel two-stage training process and a transformer-based architecture specifically designed for personalized ranking in e-commerce.
Findings
Significant offline ranking improvements using the proposed model.
Successful deployment serving millions of users daily.
Strong online A/B testing results confirming effectiveness.
Abstract
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.…
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Taxonomy
TopicsRecommender Systems and Techniques · Expert finding and Q&A systems · Information Retrieval and Search Behavior
MethodsFocus
