1 citations · 2 across the 9 of their papers we have counts for
9 papers
Sona Technical Report
Sona Team, Alexandr Udeneev, Aleksei Krasilnikov +32
We introduce Sona, a single-model generative recommender for Yandex Music. In an online A/B test, Sona replaced the entire production cascade, comprising more than 15 candidate gen…
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…
Mitigating Collaborative Semantic ID Staleness in Generative Retrieval
Vladimir Baikalov, Iskander Bagautdinov, Sergey Muravyov
Generative retrieval with Semantic IDs (SIDs) assigns each item a discrete identifier and treats retrieval as a sequence generation problem rather than a nearest-neighbor search. W…
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…
Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval
A. Ploshkin, V. Tytskiy, A. Pismenny +6
We present Yambda-5B, a large-scale open dataset sourced from the Yandex Music streaming platform. Yambda-5B contains 4.79 billion user-item interactions from 1 million users acros…
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Vladimir Baikalov, Evgeny Frolov
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships withi…