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- Lomonosov Moscow State UniversityRU146 papers
- Yandex School of Data AnalysisRU143 papers
- Centre National de la Recherche ScientifiqueFR136 papers
- University of ZurichCH136 papers
- European Organization for Nuclear ResearchCH135 papers
- Imperial College LondonGB134 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR134 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di BolognaIT134 papers
- Laboratoire de Physique Nucléaire et de Hautes ÉnergiesFR134 papers
- École Polytechnique Fédérale de LausanneCH133 papers
- Institute for High Energy PhysicsES133 papers
- Institute for Nuclear ResearchRU133 papers
255 papers
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…
fabric-lib: RDMA Point-to-Point Communication for LLM Systems
Nandor Licker, Kevin Hu, Vladimir Zaytsev +1
Emerging Large Language Model (LLM) system patterns, such as disaggregated inference, Mixture-of-Experts (MoE) routing, and asynchronous reinforcement fine-tuning, require flexible…
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…
eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion
Daria Tikhonovich, Nikita Zelinskiy, Aleksandr V. Petrov +4
Since their introduction, Transformer-based models, such as SASRec and BERT4Rec, have become common baselines for sequential recommendations, surpassing earlier neural and non-neur…
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…