Short-video recommendation presents unique challenges, such as modeling rapid user interest shifts from implicit feedback, but progress is constrained by a lack of large-scale open datasets that reflect real-world platform dynamics. To bridge this gap, we introduce the VK Large Short-Video Dataset (VK-LSVD), the largest publicly available industrial dataset of its kind. VK-LSVD offers an unprecedented scale of over 40 billion interactions from 10 million users and almost 20 million videos over six months, alongside rich features including content embeddings, diverse feedback signals, and contextual metadata. Our analysis supports the dataset's quality and diversity. The dataset's immediate impact is confirmed by its central role in the live VK RecSys Challenge 2025. VK-LSVD provides a vital, open dataset to use in building realistic benchmarks to accelerate research in sequential recommendation, cold-start scenarios, and next-generation recommender systems.

翻译：短视频推荐面临独特的挑战，例如基于隐式反馈建模用户兴趣的快速变化，但由于缺乏反映真实平台动态的大规模开放数据集，其进展受到限制。为填补这一空白，我们推出了VK大型短视频数据集（VK-LSVD），这是目前同类中规模最大的公开工业数据集。VK-LSVD提供了前所未有的规模，包含六个月内来自1000万用户与近2000万视频的超过400亿次交互，同时提供了丰富的特征，包括内容嵌入、多样化的反馈信号以及上下文元数据。我们的分析验证了该数据集的质量与多样性。该数据集的即时影响力已通过其在2025年VK推荐系统实时挑战赛中的核心作用得到证实。VK-LSVD为构建贴近现实的基准测试提供了一个至关重要的开放数据集，将加速序列推荐、冷启动场景以及下一代推荐系统的研究。