KuaiLive-M3：用于直播推荐的多模态、多领域和多反馈数据集
KuaiLive-M3: A Multi-Modal, Multi-Domain, and Multi-Feedback Dataset for Live Streaming Recommendation
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中文总结 AI 辅助
针对现有直播数据集的局限，引入KuaiLive-M3数据集，它涵盖多领域用户交互及多模态内容，有丰富反馈记录。基于此建立多种推荐基准，经实验验证其为直播推荐研究提供了有挑战且现实的基准，凸显相关研究要点。
中文摘要 AI 辅助
现有的公共直播数据集存在三大局限：对随时间演变的多模态直播内容访问受限；忽视用户在短视频和直播之间的跨域交互；仅包含隐式行为信号，缺乏明确反馈。为解决这些问题，我们引入了KuaiLive-M3数据集，它来自中国领先的直播和短视频平台快手。该数据集涵盖21938名用户，包含3500万次直播交互和1.11亿次短视频交互，还有细粒度时间戳和多样用户行为。它还提供约8800万个带时间戳的片段级多模态嵌入以及25403条基于问卷的反馈记录。基于这些独特信号，我们建立了跨域推荐、直播亮点预测和问卷增强推荐的基准。大量实验表明，KuaiLive-M3为未来直播推荐研究提供了具有挑战性和现实意义的基准，凸显了对随时间演变内容建模、跨域转移用户偏好以及弥合隐式行为与明确用户反馈差距的重要性。数据集和基准代码可公开获取。
英文摘要
Existing public live streaming datasets suffer from three major limitations: they provide limited access to temporally evolving multimodal live content, overlook users' cross-domain interactions between short videos and live streams, and contain only implicit behavioral signals without explicit feedback that captures users' perceived content quality and satisfaction. These limitations prevent existing benchmarks from faithfully reflecting real-world live streaming scenarios and hinder comprehensive research on live streaming recommendation. To address these limitations, we introduce KuaiLive-M3, a multi-modal, multi-domain, and multi-feedback dataset for live streaming recommendation, collected from Kuaishou, a leading live streaming and short video platform in China. KuaiLive-M3 covers 21,938 users and contains 35 million live streaming interactions and 111 million short video interactions, with fine-grained timestamps and diverse user behaviors. It further provides approximately 88 million timestamped segment-level multi-modal embeddings that capture the temporal evolution of live streaming content, as well as 25,403 questionnaire-based feedback records that bridge implicit user behaviors and explicit user preferences. Based on these unique signals, we establish benchmarks for cross-domain recommendation, live stream highlight prediction, and questionnaire-enhanced recommendation. Extensive experiments with representative baselines demonstrate that KuaiLive-M3 provides a challenging and realistic benchmark for future live streaming recommendation research. The results further highlight the importance of modeling temporally evolving content, transferring user preferences across domains, and bridging the gap between implicit behaviors and explicit user feedback. The dataset and benchmark code are publicly available at https://imgkkk574.github.io/KuaiLive-M3/.
