Publications

You can also find my articles on my Google Scholar profile.

Selected Publications

Long-Term Optimization for Large-Scale Generative Retrieval with Off-Policy REINFORCE Permalink

Published in KDD 2026, 5th Workshop on End-to-End Customer Journey Optimization

Artem Matveev, Sergei Makeev, Aleksei Krasilnikov, Vladimir Baikalov, Sergei Liamaev, Kirill Khrylchenko

Training generative retrievers with off-policy REINFORCE to optimize long-term user satisfaction instead of next-item accuracy, with doubly robust evaluation and test-time scaling that improves cumulative session reward on Yambda-5B.

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Gated Bidirectional Linear Attention for Generative Retrieval Permalink

Published in SIGIR 2026

Artem Matveev, Vladislav Tytskiy, Sergei Makeev, Sergei Liamaev

A linear-time bidirectional attention layer (GBLA) for generative retrieval that matches full self-attention quality while reaching up to 8.2× single-layer speedup on very long user histories.

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Scaling Recommender Transformers to One Billion Parameters Permalink

Published in KDD 2026, Jeju Island, Republic of Korea

Kirill Khrylchenko, Artem Matveev, Sergei Makeev, Vladimir Baikalov

A recipe for scaling generative-recommender transformers to one billion parameters by decomposing autoregressive user-history modeling into feedback and next-item prediction; deployed on a large-scale music platform for +2.26% listening time and +6.37% likes in online A/B tests.

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Correcting the LogQ Correction: Revisiting Sampled Softmax for Large-Scale Retrieval Permalink

Published in RecSys 2025

Kirill Khrylchenko, Vladimir Baikalov, Sergei Makeev, Artem Matveev, Sergei Liamaev

A refined logQ correction for in-batch sampled softmax that removes the residual bias from the always-present positive item, giving consistent gains for large-scale two-tower retrieval on public and proprietary datasets.

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Preprints

Sona Technical Report Permalink

Published in arXiv preprint

Sona Team: Alexandr Udeneev, Aleksei Krasilnikov, Alexey Nadtochiy, Andrey Semenov, Andrey Tsyrkunov, Anna Krivonos, Anna Lipkina, Artem Matveev, et al. (34 authors)

A single generative recommender that replaced the entire production cascade at Yandex Music — 15+ candidate generators plus pre-ranking and ranking models — while lifting Active Users by 4.53%, Total Listening Time by 6.30%, and Likes by 11.42% in an online A/B test.

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Embedding Items at Scale: Comparing GNN-Based and ID-Based Item Embeddings in the Yandex Ecosystem Permalink

Published in arXiv preprint

Sergei Makeev, Artem Matveev, Vladimir Baikalov, Kirill Khrylchenko

An industrial case study comparing pretrained GNN item embeddings with end-to-end trainable ID embeddings for transformer-based sequential recommenders across Yandex Market, Yandex Music, and a public low-resource Yandex Lavka dataset.

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