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arxiv: 2508.05633 · v2 · submitted 2025-08-07 · 💻 cs.IR · cs.AI

KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation

Changle Qu , Sunhao Dai , Ke Guo , Xiao Zhang , Liqin Zhao , Shijun Wang , Yannan Niu , Lantao Hu
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Han Li Jun Xu
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Pith reviewed 2026-05-18 23:59 UTC · model grok-4.3

classification 💻 cs.IR cs.AI
keywords live streaming recommendationreal-time datasetuser interactionsdynamic candidate itemsmulti-behavior modelingclick-through rate predictionrecommendation benchmark
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The pith

KuaiLive supplies the first public dataset with exact live room start and end times plus multi-type real-time interactions from a major streaming platform.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Live streaming recommendation differs from traditional settings because content changes in real time and users interact through clicks, comments, likes, and gifts. Existing public datasets lack the precise timestamps and behavioral variety needed to simulate these dynamics accurately. This paper releases KuaiLive, drawn from Kuaishou over 21 days and covering 23,772 users with 452,621 streamers. The dataset records live-room boundaries, four interaction types, and rich user and streamer features. These records let researchers run more realistic experiments on dynamic candidate sets and multi-behavior modeling while supplying a benchmark for standard methods.

Core claim

KuaiLive is the first real-time, interactive dataset collected from a leading live streaming platform that 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, enabling more realistic simulation of dynamic candidate items and better modeling of user and streamer behaviors.

What carries the argument

The KuaiLive dataset, built around precise live-room timestamps and four distinct real-time interaction types plus side information, supplies the concrete records that allow dynamic candidate simulation.

If this is right

  • Supports top-K recommendation, click-through rate prediction, watch-time prediction, and gift-price prediction under live conditions.
  • Enables studies of multi-behavior modeling that combine clicks, comments, likes, and gifts.
  • Allows multi-task learning experiments that jointly optimize several prediction targets.
  • Provides side information suitable for fairness-aware recommendation research.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The timestamped interaction sequences could be used to test algorithms that must adapt to sudden changes in available items.
  • Similar fine-grained logs might help identify engagement patterns that appear only when live content evolves continuously.
  • The dataset structure invites direct comparison of model robustness between live streaming and static video or product recommendation settings.

Load-bearing premise

Data gathered from one Chinese platform across a 21-day window represents typical live-streaming behavior without large platform-specific biases or missing interactions.

What would settle it

Training a recommendation model on KuaiLive and then measuring a sharp drop in performance when the same model is tested on interaction logs from a different live-streaming service would indicate the dataset does not generalize.

Figures

Figures reproduced from arXiv: 2508.05633 by Changle Qu, Han Li, Jun Xu, Ke Guo, Lantao Hu, Liqin Zhao, Shijun Wang, Sunhao Dai, Xiao Zhang, Yannan Niu.

Figure 1
Figure 1. Figure 1: Illustration of live streaming scenarios in Kuaishou [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Analysis of streamer behaviors in the KuaiLive dataset. (a) shows the distribution of interaction counts per streamer. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Analysis of user interactions in the KuaiLive dataset. (a) shows the distribution of interaction counts per user. (b) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces KuaiLive, a public dataset collected from the Kuaishou live streaming platform. It records interactions from 23,772 users and 452,621 streamers over 21 days, including precise live room start/end timestamps, multi-type real-time interactions (click, comment, like, gift), and rich side information for users and streamers. The authors provide multi-perspective analysis and benchmark representative recommendation methods to support tasks such as top-K recommendation, CTR prediction, watch time prediction, gift price prediction, multi-behavior modeling, multi-task learning, and fairness-aware recommendation.

Significance. If the collected data faithfully captures live streaming dynamics, KuaiLive fills a notable gap by supplying the first public resource with fine-grained temporal and multi-behavior features for this domain. The public release, combined with benchmark results and support for diverse tasks, positions it as a useful foundation for advancing research on dynamic candidate sets and real-time user/streamer behaviors.

major comments (1)
  1. Abstract: The central claim that the dataset 'enables more realistic simulation of dynamic candidate items' due to precise timestamps and real-time interactions is load-bearing but unsupported by direct evidence. The described benchmarks evaluate standard methods without an ablation or comparison demonstrating improved simulation fidelity attributable to these features versus prior datasets lacking them.
minor comments (2)
  1. Abstract: Add a brief limitations paragraph noting the 21-day single-platform collection window and any potential platform-specific biases in interaction patterns (e.g., gift or comment mechanics) to help readers assess generalizability.
  2. Dataset construction section: Provide additional details on data cleaning steps, filtering criteria, and coverage statistics (e.g., fraction of interactions retained) to allow verification of completeness and reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address the single major comment point by point below, with an honest assessment of the manuscript's current support for the claim in question.

read point-by-point responses

  1. Referee: Abstract: The central claim that the dataset 'enables more realistic simulation of dynamic candidate items' due to precise timestamps and real-time interactions is load-bearing but unsupported by direct evidence. The described benchmarks evaluate standard methods without an ablation or comparison demonstrating improved simulation fidelity attributable to these features versus prior datasets lacking them.

    Authors: We thank the referee for this observation. The abstract claim is grounded in the dataset's distinguishing characteristics: precise live-room start/end timestamps and multi-type timestamped interactions (click, comment, like, gift) that allow candidate sets to be reconstructed as they evolve in real time, in contrast to the static item pools typical of prior public datasets. The multi-perspective analysis and the reported benchmarks on tasks such as CTR prediction, watch-time prediction, and multi-behavior modeling demonstrate that these features can be directly exploited by standard methods. Nevertheless, we acknowledge that the current experiments do not contain an explicit ablation or cross-dataset comparison that quantifies an improvement in simulation fidelity. To address the concern without overstating the evidence, we will revise the abstract to replace 'enables more realistic simulation' with the more precise phrasing 'facilitates more realistic simulation of dynamic candidate items by providing precise timestamps and real-time interaction logs.' We will also add one clarifying sentence in Section 3 (Dataset Description) that explicitly ties the claim to the data properties rather than to new empirical results. These changes constitute a minor revision that directly responds to the comment. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset release with independent empirical contribution

full rationale

KuaiLive is a data-collection paper whose central claims rest on the described properties of the released logs (precise timestamps, multi-type interactions, side information) rather than any derivation, equation, or fitted parameter. No self-definitional loops, fitted-input predictions, or load-bearing self-citations appear in the provided text; the 'first' and 'more realistic' assertions are direct descriptions of the collected data, not reductions to prior author work or internal fits. The 21-day Kuaishou sample's representativeness is an external assumption open to falsification, not a circular step inside the paper's own chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the collected logs faithfully represent live-streaming dynamics and that the 21-day window plus the chosen user and streamer samples are sufficient for realistic simulation.

axioms (1)
  • domain assumption The interaction logs collected from Kuaishou accurately capture all relevant real-time user and streamer behaviors without material platform-specific bias or missing data.
    Invoked when claiming the dataset enables realistic simulation of dynamic candidate items and user behaviors.

pith-pipeline@v0.9.0 · 5852 in / 1323 out tokens · 47773 ms · 2026-05-18T23:59:16.413869+00:00 · methodology

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