From the 1 of 7 linked papers with an AI index.
7 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…
Gated Bidirectional Linear Attention for Generative Retrieval
Artem Matveev, Vladislav Tytskiy, Sergei Makeev +1
In recommender systems, generative retrieval typically uses an encoder-decoder setup: an encoder processes a user interaction history, and an autoregressive decoder then generates…
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…