1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…