# A Large-scale Dataset with Behavior, Attributes, and Content of Mobile Short-video Platform

arXiv: https://arxiv.org/abs/2502.05922 · HTML: https://mlsanity.com/paper/2502.05922/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2025.

## Built on by (most cited first, 8 of 8)

- [AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration](https://mlsanity.com/paper/2510.10395/) (2025)
- [AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems](https://mlsanity.com/paper/2505.19623/) (2025)
- [DiaDem: Advancing Dialogue Descriptions in Audiovisual Video Captioning for Multimodal Large Language Models](https://mlsanity.com/paper/2601.19267/) (2026)
- [Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning](https://mlsanity.com/paper/2607.01667/) (2026)
- [MealRec: Multi-granularity Sequential Modeling via Hierarchical Diffusion Models for Micro-Video Recommendation](https://mlsanity.com/paper/2603.01926/) (2026)
- [Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation](https://mlsanity.com/paper/2605.08810/) (2026)
- [Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy](https://mlsanity.com/paper/2608.13990/) (2026)
- [Preference Flow Matching with Spectral Factorization for Micro-video Recommendation](https://mlsanity.com/paper/2608.26579/) (2026)

## Abstract (arXiv)

Short-video platforms show an increasing impact on people's daily lives nowadays, with billions of active users spending plenty of time each day. The interactions between users and online platforms give rise to many scientific problems across computational social science and artificial intelligence. However, despite the rapid development of short-video platforms, currently there are serious shortcomings in existing relevant datasets on three aspects: inadequate user-video feedback, limited user attributes and lack of video content. To address these problems, we provide a large-scale dataset with rich user behavior, attributes and video content from a real mobile short-video platform. This dataset covers 10,000 voluntary users and 153,561 videos, and we conduct four-fold technical validations of the dataset. First, we verify the richness of the behavior and attribute data. Second, we confirm the representing ability of the content features. Third, we provide benchmarking results on recommendation algorithms with our dataset. Finally, we explore the filter bubble phenomenon on the platform using the dataset. We believe the dataset could support the broad research community, including but not limited to user modeling, social science, human behavior understanding, etc. The dataset and code is available at https://github.com/tsinghua-fib-lab/ShortVideo_dataset.