# KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation arXiv: https://arxiv.org/abs/2508.05633 ยท HTML: https://mlsanity.com/paper/2508.05633/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 59 references that are in the map. - First posted on arXiv 7 Aug 2025. ## Builds on (most cited first, 30 of 37) - [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018) - [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020) - [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017) - [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016) - [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015) - [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017) - [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017) - [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018) - [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017) - [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018) - [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020) - [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018) - [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017) - [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023) - [Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://mlsanity.com/paper/2311.09049/) (2023) - [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025) - [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019) - [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019) - [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022) - [Towards Representation Alignment and Uniformity in Collaborative Filtering](https://mlsanity.com/paper/2206.12811/) (2022) - [MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation](https://mlsanity.com/paper/2007.03183/) (2020) - [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022) - [Controllable Multi-Objective Re-ranking with Policy Hypernetworks](https://mlsanity.com/paper/2306.05118/) (2023) - [KuaiSAR: A Unified Search And Recommendation Dataset](https://mlsanity.com/paper/2306.07705/) (2023) - [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024) - [Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation](https://mlsanity.com/paper/2106.03415/) (2021) - [P-MMF: Provider Max-min Fairness Re-ranking in Recommender System](https://mlsanity.com/paper/2303.06660/) (2023) - [Reward Imputation with Sketching for Contextual Batched Bandits](https://mlsanity.com/paper/2210.06719/) (2022) - [ReCODE: Modeling Repeat Consumption with Neural ODE](https://mlsanity.com/paper/2405.16550/) (2024) - [ReChorus2.0: A Modular and Task-Flexible Recommendation Library](https://mlsanity.com/paper/2405.18058/) (2024) ## Built on by (most cited first, 7 of 7) - [Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces](https://mlsanity.com/paper/2604.08362/) (2026) - [Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models](https://mlsanity.com/paper/2601.13533/) (2026) - [Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking](https://mlsanity.com/paper/2604.07420/) (2026) - [All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models](https://mlsanity.com/paper/2607.09502/) (2026) - [Scaling Articulated Rationales for MLLM-based Recommendation](https://mlsanity.com/paper/2609.17639/) (2026) - [Live Assistant: Learning Whether, When, and Whom to Assist in Real-World Live Social Streams](https://mlsanity.com/paper/2609.27303/) (2026) - [Discovered, Not Designed: Population Evolution for Collaborative and Compute-Intensive Model Discovery](https://mlsanity.com/paper/2610.05950/) (2026) ## Abstract (arXiv) 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.