# A Content-Driven Micro-Video Recommendation Dataset at Scale

arXiv: https://arxiv.org/abs/2309.15379 · HTML: https://mlsanity.com/paper/2309.15379/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2023.

## Built on by (most cited first, 30 of 45)

- [Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2408.09698/) (2024)
- [IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT](https://mlsanity.com/paper/2404.02059/) (2024)
- [Personalized Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2412.02142/) (2024)
- [Graph World Model](https://mlsanity.com/paper/2507.10539/) (2025)
- [A Survey on Safe Multi-Modal Learning System](https://mlsanity.com/paper/2402.05355/) (2024)
- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation](https://mlsanity.com/paper/2411.02992/) (2024)
- [MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling](https://mlsanity.com/paper/2512.07216/) (2025)
- [DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos](https://mlsanity.com/paper/2505.16376/) (2025)
- [LinkedOut: Linking World Knowledge Representation Out of Video LLM for Next-Generation Video Recommendation](https://mlsanity.com/paper/2512.16891/) (2025)
- [SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation](https://mlsanity.com/paper/2405.00287/) (2024)
- [Described Spatial-Temporal Video Detection](https://mlsanity.com/paper/2407.05610/) (2024)
- [Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation](https://mlsanity.com/paper/2503.05493/) (2025)
- [RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation](https://mlsanity.com/paper/2506.20817/) (2025)
- [LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation](https://mlsanity.com/paper/2502.12945/) (2025)
- [G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation](https://mlsanity.com/paper/2508.05709/) (2025)
- [Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts](https://mlsanity.com/paper/2508.05993/) (2025)
- [Semantic Item Graph Enhancement for Multimodal Recommendation](https://mlsanity.com/paper/2508.06154/) (2025)
- [Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations](https://mlsanity.com/paper/2508.09789/) (2025)
- [RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation](https://mlsanity.com/paper/2509.03131/) (2025)
- [Verifiable Reasoning for LLM-based Generative Recommendation](https://mlsanity.com/paper/2603.07725/) (2026)
- [ATFLRec: A Multimodal Recommender System with Audio-Text Fusion and Low-Rank Adaptation via Instruction-Tuned Large Language Model](https://mlsanity.com/paper/2409.08543/) (2024)
- [M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction](https://mlsanity.com/paper/2411.15455/) (2024)
- [ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations](https://mlsanity.com/paper/2508.05667/) (2025)
- [ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests](https://mlsanity.com/paper/2510.26095/) (2025)
- [Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation](https://mlsanity.com/paper/2511.18740/) (2025)
- [MMSRARec: Summarization and Retrieval Augumented Sequential Recommendation Based on Multimodal Large Language Model](https://mlsanity.com/paper/2512.20916/) (2025)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [ARTAI: An Evaluation Platform to Assess Societal Risk of Recommender Algorithms](https://mlsanity.com/paper/2409.12396/) (2024)
- [Token-Efficient Item Representation via Images for LLM Recommender Systems](https://mlsanity.com/paper/2503.06238/) (2025)

## Abstract (arXiv)

Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries. However, the lack of large-scale public micro-video datasets poses a major challenge for developing effective recommender systems. To address this challenge, we introduce a very large micro-video recommendation dataset, named "MicroLens", consisting of one billion user-item interaction behaviors, 34 million users, and one million micro-videos. This dataset also contains various raw modality information about videos, including titles, cover images, audio, and full-length videos. MicroLens serves as a benchmark for content-driven micro-video recommendation, enabling researchers to utilize various modalities of video information for recommendation, rather than relying solely on item IDs or off-the-shelf video features extracted from a pre-trained network. Our benchmarking of multiple recommender models and video encoders on MicroLens has yielded valuable insights into the performance of micro-video recommendation. We believe that this dataset will not only benefit the recommender system community but also promote the development of the video understanding field. Our datasets and code are available at https://github.com/westlake-repl/MicroLens.