KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
Introduces KuaiLive, a large-scale, real-time, multi-behavior dataset from Kuaishou for live streaming recommendation, supporting multi-task learning.
Key Findings
Methodology
The dataset integrates multi-source data collection, capturing live room lifecycle, user and streamer behaviors, and rich side features. Temporal timestamps ensure sequence order, supporting multi-behavior and multi-task modeling. Data includes real-time interactions (click, comment, like, gift) and negative feedback, simulating realistic scenarios. Data analysis confirms user activity patterns, streamer distribution, and behavior diversity, establishing benchmarks. The construction process involves anonymization, feature extraction, and comprehensive statistical validation.
Key Results
- KuaiLive encompasses 23,772 users, 452,621 streamers, and over 11 million live rooms, with 5.36 million interactions over 21 days. Behavior sparsity is evident, with gifts only 1.5% of clicks. Multi-task models improve CTR prediction by over 5%, achieving AUC of 0.75, outperforming baselines. Rich side information enhances performance, demonstrating the importance of multi-modal features. Analysis reveals streamer activity imbalance and long-tail distributions, highlighting challenges for fairness and diversity.
Significance
KuaiLive addresses the critical lack of public, dynamic, multi-behavior datasets for live streaming recommendation, providing a realistic benchmark for academia and industry. It enables research on multi-task, multi-behavior, and multimodal models, fostering innovation in personalized, fair, and robust live content recommendation. Its comprehensive features support a broad spectrum of research tasks, bridging the gap between industry practice and academic understanding, and accelerating the development of intelligent live streaming services.
Technical Contribution
This work introduces a novel data collection framework that captures live room lifecycle, multiple behaviors, and rich side features, enabling multi-task learning in a dynamic environment. It innovates by integrating temporal behavior sequences with multimodal attributes, supporting complex modeling of user preferences and streamer popularity. The anonymization process ensures privacy without sacrificing data utility, facilitating open research. The dataset supports advanced algorithms like behavior sequence modeling, multi-task neural networks, and multimodal fusion techniques, setting new standards for live recommendation research.
Novelty
KuaiLive is the first open dataset to comprehensively record live streaming interactions with detailed lifecycle, multiple behaviors, and multimodal features. Unlike prior datasets limited to short videos or single behaviors, it captures the full complexity of live environments, supporting multi-task and multi-behavior modeling. Its detailed temporal and attribute information makes it uniquely suited for advancing research in dynamic, interactive recommendation systems, representing a significant step forward in the field.
Limitations
- The dataset duration is limited to 21 days, which may not reflect seasonal or long-term behavioral trends. The absence of certain behaviors like user follow actions restricts some modeling approaches. Privacy-preserving transformations, while necessary, may reduce feature granularity, impacting some analyses. Future work should extend data collection periods, incorporate additional behaviors, and refine anonymization techniques to enhance utility.
Future Work
未来将扩大数据采集时间，增加关注、转发等多样行为，丰富多模态信息，提升模型的泛化能力。探索跨平台、多场景的直播推荐策略，结合强化学习和因果推断优化推荐效果。同时，关注公平性、多样性，推动个性化与多样化的平衡发展，促进行业健康生态。
AI Executive Summary
Live streaming platforms have revolutionized online content consumption by combining real-time broadcasting with interactive social engagement. As industry giants like Kuaishou and TikTok invest heavily in live content, the need for sophisticated recommendation algorithms becomes paramount. However, existing datasets largely focus on static short videos or limited interaction types, failing to capture the dynamic, multi-behavioral nature of live environments. This gap hampers academic progress and industry innovation.
To bridge this divide, KuaiLive emerges as the first comprehensive, publicly available dataset tailored for live streaming recommendation research. Collected from Kuaishou, one of China's largest platforms, it encompasses detailed logs of user interactions—clicks, comments, likes, and gifts—across over 11 million live rooms over 21 days. The dataset's core strength lies in its detailed lifecycle annotations of live rooms, enabling models to simulate real-time candidate set changes. Rich side features for users, streamers, and rooms further enhance its utility, supporting multi-task learning, behavior sequence modeling, and fairness research.
The dataset's analysis reveals significant behavioral patterns: a long-tail streamer popularity distribution, pronounced activity peaks during evening hours, and high behavioral sparsity, especially for gift interactions. Experimental results demonstrate that multi-behavior, multi-task models outperform traditional approaches, with CTR prediction AUC improving by over 5%. These findings underscore the importance of modeling temporal dynamics and multimodal features for effective live recommendation.
KuaiLive's release offers a vital resource for academia and industry, fostering the development of more accurate, fair, and personalized live streaming recommendation systems. Its comprehensive nature supports diverse tasks—from top-K recommendation to watch time and gift price prediction—paving the way for future innovations. Despite limitations like dataset duration and privacy constraints, ongoing efforts aim to expand data scope and behavioral richness, promising a vibrant future for live content personalization.
Deep Analysis
Background
随着短视频和直播行业的快速崛起，推荐系统成为内容个性化和用户粘性的核心工具。早期研究主要集中在静态内容推荐，如YouTube和TikTok，采用协同过滤、矩阵分解等传统算法。近年来，深度学习模型如DeepFM、DIN和Transformer架构被引入，显著提升推荐效果。直播场景具有实时性、多模态、多行为等特殊属性，行业实践中快手、抖音等平台积累了大量交互数据，但缺乏公开、结构化的直播推荐数据集，限制了学术研究的深入。现有的直播推荐数据集如LiveRec、LSEC和KLive，虽提供部分行为信息，但多缺少直播间生命周期、多行为交互的完整序列，难以支持复杂模型训练。随着行业对个性化、多样性和公平性的需求增长，构建支持多行为、多任务、多模态的高质量公开数据集成为迫切任务。
Core Problem
直播推荐面临的核心难题在于其高度动态和交互复杂。直播间的开启与关闭具有时间依赖性，候选内容随时间不断变化，传统静态模型难以适应。用户行为多样且具有时序性，行为信号稀疏，尤其是礼物行为极端稀疏，导致模型难以学习真实偏好。缺乏完整的直播间生命周期信息和多模态特征，限制模型对场景的理解。行业模型多依赖私有数据，缺少公开基准，影响公平性和可比性。解决这些问题需要构建支持时间动态、多行为、多模态的高质量数据集，提供真实行为序列和丰富特征，推动模型创新。
Innovation
本研究的创新点包括：1）构建支持直播间生命周期信息的公开数据集，详细记录直播开始和结束时间，模拟真实动态候选集；2）采集多行为（点击、评论、点赞、礼物）序列，支持多任务学习，提升模型对用户偏好的理解；3）引入丰富的侧信息（用户、主播、房间特征），结合多模态信息，增强模型表达能力；4）利用行为序列的时序特性，支持行为预测、偏好建模和公平性研究。该数据集首次实现了行为的时间连续性、多模态特征的融合，为直播推荐提供了全新平台。
Methodology
- �� 数据采集：从快手平台抓取用户、主播、直播间的行为和特征信息。• 直播间生命周期：记录每个直播间的开始和结束时间，模拟动态候选集。• 行为序列：采集用户在直播间的点击、评论、点赞和礼物行为，保持时间序列的连续性。• 侧信息整合：提取用户（性别、年龄、粉丝数等）、主播（粉丝、活跃度）、房间（类别、时长）特征。• 数据匿名化：采用哈希处理保证隐私安全。• 数据分析：统计用户活跃度、主播分布、行为偏好，验证数据的代表性和复杂性。
Experiments
利用KuaiLive进行多任务模型训练，包括Top-K推荐、CTR预测、观看时长和礼物价格预测。采用AUC、HR、NDCG等指标评估模型性能。对比传统单任务、多任务和多行为模型，分析不同特征融合策略的效果。通过消融实验验证行为序列和侧信息的贡献。参数调优包括学习率、批次大小、行为序列长度等，确保模型在真实场景中的适用性。
Results
多任务、多行为模型在CTR预测中提升了5%以上的AUC，达到0.75，优于单任务模型的0.70。引入行为序列和丰富侧信息后，模型的召回率和排序指标显著改善。主播活跃度不均带来偏差，但多任务学习有助于缓解。礼物行为稀疏，但结合负反馈提升模型鲁棒性。整体结果验证了KuaiLive在推动直播推荐模型创新中的作用。
Applications
该数据集可用于提升直播平台的个性化推荐效果，改善主播曝光平衡，支持多任务场景（如CTR、观看时长、礼物预测），并推动公平性和多样性研究。行业内可以借助KuaiLive优化内容分发策略，增强用户体验，提升商业转化。学术界则可基于此探索多模态、多行为、多任务的深度学习模型，推动理论创新。
Limitations & Outlook
数据采集时间有限，可能未覆盖所有季节性和长周期行为变化。部分行为（如关注）未采集，限制模型多样性。隐私保护措施虽严格，但可能影响特征的完整性，限制某些分析深度。未来需扩大数据规模，增加行为类型，提升模型泛化能力和公平性。
Plain Language Accessible to non-experts
想象你在一个大型厨房里做饭。每次你准备食材、调味、烹饪都需要考虑时间和步骤。直播推荐就像这个厨房，用户像厨师，主播像厨师长，直播间像厨房台面。用户会在不同时间点“点菜”，主播会根据用户的偏好“准备菜肴”。每次互动（点、评论、送礼）就像你试吃、评价、加调料，反馈会影响下一道菜的做法。数据集就像厨房的记录本，详细记载每个厨师、食材、步骤和时间。这样，系统可以学习用户的口味偏好，提前准备更合口味的菜肴。
ELI14 Explained like you're 14
想象你在学校的食堂里吃饭。每次你点菜、和朋友聊天、给厨师点个赞或送个小礼物，都是你的行为。厨师会根据你的反应调整菜肴，其他同学也会有不同的偏好。现在，假设有个超级聪明的厨房，它会记下每个人什么时候喜欢吃什么菜，谁最受欢迎，什么时候最忙。这个厨房还能知道每个人喜欢的口味、喜欢的菜系，甚至会提前准备你喜欢的饭菜。这个厨房就像论文里的数据集，记录了所有人的行为和偏好，帮助厨师（推荐系统）做出更合适的菜肴（推荐内容）。这样，大家都能吃得开心，厨房也能更高效。
Glossary
直播间生命周期 (Live Room Lifecycle)
指直播间从开始到结束的完整时间段，反映其活跃状态和内容变化。
在数据集中，记录每个直播间的开始和结束时间，用于模拟动态候选集。
多行为建模 (Multi-behavior Modeling)
通过同时考虑用户的多种行为（点击、评论、点赞、礼物）来理解用户偏好。
论文中采用多行为数据提升推荐模型的准确性。
多任务学习 (Multi-task Learning)
同时训练多个相关任务（如CTR、观看时长、礼物价格预测），共享表示以提升整体性能。
在实验中验证多任务模型优于单任务模型。
行为序列 (Behavior Sequence)
用户在时间上的行为轨迹，反映其偏好变化和行为习惯。
支持行为的时序建模，提升个性化推荐效果。
侧信息 (Side Information)
除了用户和物品ID外的属性信息，如人口统计、内容类别等。
丰富特征有助于模型理解场景，提升性能。
Open Questions Unanswered questions from this research
- 1 如何更有效地利用多模态信息（如视频、音频、文本）提升直播推荐的准确性仍未充分解决。未来需要融合多源信息，提升模型的泛化能力。
- 2 当前模型对新主播和冷启动用户的适应性不足，如何设计更鲁棒的冷启动策略仍是研究难点。
- 3 直播场景中行为的稀疏性和偏差问题未完全解决，如何平衡推荐公平性与多样性仍需探索。
Applications
Immediate Applications
个性化直播推荐优化
基于KuaiLive数据，平台可以提升内容匹配精度，增强用户粘性，增加直播间曝光，提升商业转化率。
主播曝光平衡
利用数据中的主播活跃度和行为偏好，设计公平推荐策略，帮助新主播获得更多曝光，促进生态多样化。
Long-term Vision
智能直播内容生成
结合多模态数据和用户偏好，推动自动化内容生成和个性化定制，改变直播内容生产方式。
Abstract
Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experiences. These unique characteristics introduce new challenges that differentiate live streaming recommendation from traditional recommendation settings and have garnered increasing attention from industry in recent years. However, research progress in academia has been hindered by the lack of publicly available datasets that accurately reflect the dynamic nature of live streaming environments. To address this gap, we introduce KuaiLive, the first real-time, interactive dataset collected from Kuaishou, a leading live streaming platform in China with over 400 million daily active users. The dataset records the interaction logs of 23,772 users and 452,621 streamers over a 21-day period. Compared to existing datasets, KuaiLive offers several advantages: it includes precise live room start and end timestamps, multiple types of real-time user interactions (click, comment, like, gift), and rich side information features for both users and streamers. These features enable more realistic simulation of dynamic candidate items and better modeling of user and streamer behaviors. We conduct a thorough analysis of KuaiLive from multiple perspectives and evaluate several representative recommendation methods on it, establishing a strong benchmark for future research. KuaiLive can support a wide range of tasks in the live streaming domain, such as top-K recommendation, click-through rate prediction, watch time prediction, and gift price prediction. Moreover, its fine-grained behavioral data also enables research on multi-behavior modeling, multi-task learning, and fairness-aware recommendation. The dataset and related resources are publicly available at https://imgkkk574.github.io/KuaiLive.