Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs

• URL: http://arxiv.org/abs/2509.09682v2
• Date: Fri, 24 Oct 2025 16:19:07 GMT
• Title: Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
• Authors: Maxim Zhelnin, Dmitry Redko, Volkov Daniil, Anna Volodkevich, Petr Sokerin, Valeriy Shevchenko, Egor Shvetsov, Alexey Vasilev, Darya Denisova, Ruslan Izmailov, Alexey Zaytsev,
• Abstract summary: We introduce the CCE- method, which offers a GPU-efficient implementation of the cross-entropy loss with negative sampling.<n>Our method accelerates training by up to two times while reducing memory consumption by more than 10 times.
• Score: 3.0832329178398967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-changing user preferences. However, training transformer-based SR models often encounters a high computational cost associated with scoring extensive item catalogs, often exceeding thousands of items. This occurs mainly due to the use of cross-entropy loss, where peak memory scales proportionally to catalog size, batch size, and sequence length. Recognizing this, practitioners in the field of recommendation systems typically address memory consumption by integrating the cross-entropy (CE) loss with negative sampling, thereby reducing the explicit memory demands of the final layer. However, a small number of negative samples would degrade model performance, and as we demonstrate in our work, increasing the number of negative samples and the batch size further improves the model's performance, but rapidly starts to exceed industrial GPUs' size (~40Gb). In this work, we introduce the CCE- method, which offers a GPU-efficient implementation of the CE loss with negative sampling. Our method accelerates training by up to two times while reducing memory consumption by more than 10 times. Leveraging the memory savings afforded by using CCE- for model training, it becomes feasible to enhance its accuracy on datasets with a large item catalog compared to those trained with original PyTorch-implemented loss functions. Finally, we perform an analysis of key memory-related hyperparameters and highlight the necessity of a delicate balance among these factors. We demonstrate that scaling both the number of negative samples and batch size leads to better results rather than maximizing only one of them. To facilitate further adoption of CCE-, we release a Triton kernel that efficiently implements the proposed method.

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