1 citations · 1 across the 7 of their papers we have counts for
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Embedding Items at Scale: Comparing GNN-Based and ID-Based Item Embeddings in the Yandex Ecosystem
Sergei Makeev, Artem Matveev, Vladimir Baikalov +1
Transformer-based sequential recommendation models, which process sequences of user-item interactions, rely heavily on the item embedding strategy. Existing approaches either use p…
Session-Level Optimization for Large-Scale Retrieval using REINFORCE with Multi-Step Off-Policy Correction
Artem Matveev, Sergei Makeev, Aleksei Krasilnikov +3
Two-tower models are a widely used paradigm for large-scale retrieval in recommendation. However, they are typically trained with myopic supervised objectives, such as next-item pr…
Variable-Length Semantic IDs for Recommender Systems
Kirill Khrylchenko
Generative models are increasingly used in recommender systems, both for modeling user behavior as event sequences and for integrating large language models into recommendation pip…
Blending Sequential Embeddings, Graphs, and Engineered Features: 4th Place Solution in RecSys Challenge 2025
Sergei Makeev, Alexandr Andreev, Vladimir Baikalov +3
This paper describes the 4th-place solution by team ambitious for the RecSys Challenge 2025, organized by Synerise and ACM RecSys, which focused on universal behavioral modeling. T…
Correcting the LogQ Correction: Revisiting Sampled Softmax for Large-Scale Retrieval
Kirill Khrylchenko, Vladimir Baikalov, Sergei Makeev +2
Two-tower neural networks are a popular architecture for the retrieval stage in recommender systems. These models are typically trained with a softmax loss over the item catalog. H…
Scaling Recommender Transformers to One Billion Parameters
Kirill Khrylchenko, Artem Matveev, Sergei Makeev +1
While large transformer models have been successfully used in many real-world applications such as natural language processing, computer vision, and speech processing, scaling tran…
Personalized Transformer-based Ranking for e-Commerce at Yandex
Kirill Khrylchenko, Alexander Fritzler
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…