From the 1 of 8 linked papers with an AI index.
1 citations · 1 across the 5 of their papers we have counts for
8 papers
Sona Technical Report
Sona Team, Alexandr Udeneev, Aleksei Krasilnikov +33
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
The paper compares pretrained graph neural network item embeddings with end‑to‑end trainable embeddings in transformer‑based sequential recommendation systems at Yandex, finding pr…
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…
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…
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…