KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
In recent years, live streaming platforms have surged in popularity, establishing themselves as a primary mode of online content consumption. These platforms offer users a dynamic and interactive experience that traditional media cannot replicate. However, the unique characteristics of live streaming introduce challenges for recommendation systems, which differ significantly from those used in conventional settings. As the demand for efficient recommendation algorithms grows, so does the need for datasets that reflect the intricacies of live streaming environments.
To bridge this gap, a new dataset known as KuaiLive has been introduced. This dataset is a pioneering real-time, interactive resource collected from Kuaishou, a leading live streaming platform in China that boasts over 400 million daily active users. KuaiLive is set to transform academic research by providing the necessary data to develop and evaluate innovative recommendation systems tailored for live streaming.
Key Features of KuaiLive
KuaiLive distinguishes itself from existing datasets through several notable features:
- Comprehensive User Interaction Logs: The dataset records interaction logs for 23,772 users and 452,621 streamers over a 21-day period, capturing a wealth of real-time user engagement.
- Precise Timestamps: KuaiLive includes accurate start and end timestamps for live streams, allowing for a detailed analysis of viewer behavior over time.
- Diverse Interaction Types: It encompasses multiple types of real-time user interactions, including clicks, comments, likes, and gifts, providing a rich context for behavioral analysis.
- Rich Side Information: The dataset offers extensive side information features for both users and streamers, enabling researchers to model various factors influencing user preferences.
These features facilitate a more realistic simulation of dynamic candidate items and enhance the modeling of user and streamer behaviors, which is crucial for developing robust recommendation systems.
Applications and Research Opportunities
KuaiLive supports a diverse range of tasks within the live streaming domain, opening new avenues for research and practical applications. Some of the key tasks that can be tackled using this dataset include:
- Top-K Recommendation: Identifying the most relevant streams for users based on their preferences and interaction history.
- Click-Through Rate Prediction: Estimating the likelihood of users clicking on recommended streams, thereby optimizing recommendation strategies.
- Watch Time Prediction: Predicting how long users will engage with specific streams, which can inform content creation and marketing strategies.
- Gift Price Prediction: Analyzing user behaviors to predict the pricing of virtual gifts within streams.
Moreover, the fine-grained behavioral data available in KuaiLive enables research in multi-behavior modeling, multi-task learning, and fairness-aware recommendation systems, further enriching the academic discourse surrounding live streaming technologies.
Conclusion
KuaiLive stands as a significant contribution to the field of live streaming recommendation systems, providing researchers with a robust dataset to explore innovative solutions for real-time challenges. The dataset and related resources can be accessed publicly at https://imgkkk574.github.io/KuaiLive, inviting scholars and practitioners alike to leverage its potential for advancing the future of live streaming technologies.
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