---
title: 'MicroLens: Multimodal Micro-Video Dataset'
url: https://www.emergentmind.com/topics/microlens-dataset
type: topic
---

# MicroLens: Multimodal Micro-Video Dataset

MicroLens is a large-scale, multimodal micro-video recommendation dataset specifically constructed to catalyze research in content-driven micro-video recommendation systems. It provides extensive user–item interaction logs, rich video metadata, and raw media modalities for each item, facilitating benchmarking and exploration of advanced recommendation models beyond traditional ID-based collaborative filtering. MicroLens is publicly available along with code for baseline experiments and access instructions [2309.15379].

## 1. Dataset Composition and Scope

MicroLens comprises interaction events between a vast population of users and a corpus of micro-videos. The full dataset contains 34,492,051 unique users, 1,142,528 micro-videos, and 1,006,528,709 user–video interactions, yielding a sparsity of approximately 99.997%. Two research-friendly subsets are provided:

| Subset         | Users       | Videos     | Interactions | Sparsity     |
|----------------|-------------|------------|--------------|--------------|
| MicroLens-100K | 100,000     | 19,738     | 719,405      | 99.96%       |
| MicroLens-1M   | 1,000,000   | 91,402     | 9,095,620    | 99.99%       |

All interactions are derived from publicly visible user comments. For recommendation experiments, leave-one-out timestamp-based splits are applied per user: the latest comment forms the test set, the penultimate comment forms validation, and the remainder constitutes training data. Maximum user sequence length is capped at 13 (≥ 95% coverage).

## 2. Modalities and Metadata

Each micro-video in MicroLens includes the following modalities and metadata:

- **Text:** Human-written titles, at least 3 characters post-cleaning.
- **Image:** Raw cover image (JPEG/PNG), post-filtering for uniform color regions (< 75%).
- **Audio:** Native audio track (AAC/MP3, 44.1 kHz).
- **Video:** Full-length micro-video (MP4/H.264), files ≥ 100 KB.
- **Metadata:** View count, like count, video duration (∼100–400 seconds), tag IDs (fine-grained categories), and user gender.
- **User Comments:** Up to 5,000 per video, storing anonymized user_id, timestamp, and comment_text.

All IDs are anonymized by hashing, respecting user privacy and platform licensing by referencing media through URLs instead of direct distribution.

## 3. Data Acquisition and Preprocessing

The dataset was assembled via a two-stage crawl. Seed videos were identified by home-page crawl (June 2022–June 2023), filtered for high engagement (≥ 10,000 likes, typically ≥ 100 comments). For each seed, ten randomly chosen related videos were included, forming a candidate pool of ∼5 million items. Public comment pages were scraped for up to 5,000 comments per video with duplicate removal for repeated user entries.

Modalities underwent several filters:
- Titles with < 3 characters were excluded.
- Cover images dominated by uniform color (≥ 75%) were excluded.
- Video files < 100 KB were excluded.
- URLs and metadata were de-duplicated.
- All user and item identifiers were hashed.

Splits are provided for the 100K and 1M subsets, as outlined above. Licensing compliance is maintained by offering media via a Python URL resolver analogous to YouTube-8M procedures, avoiding copyright infringement.

## 4. Data Structure, Formatting, and Access

Interaction records are provided as CSV/TSV files with the following schema:

| Field       | Type     | Description                             |
|-------------|----------|-----------------------------------------|
| user_id     | int      | Hash-anonymized unique user identifier  |
| item_id     | int      | Hash-anonymized unique video identifier |
| timestamp   | int      | UNIX epoch (seconds)                    |
| comment_text| string   | UTF-8 encoded user comment              |

Additional fields (user_gender, session_id) are provided as optional columns.

“Item metadata” is stored as JSON or CSV with attributes:

| Attribute           | Format  | Description                                |
|---------------------|---------|--------------------------------------------|
| item_id             | int     | Hash-anonymized video identifier           |
| title               | string  | Video title                                |
| cover_image_url     | string  | JPEG/PNG image URL                         |
| video_url           | string  | MP4/H.264 video URL                        |
| audio_url           | string  | AAC/MP3 audio URL                          |
| view_count,like_count| int    | Engagement metrics                         |
| duration            | int     | Duration in seconds                        |
| tags                | list    | Fine-grained category IDs                  |

Multimedia files retain native platform resolutions: images (typically 720×1280 or 480×270 JPEG/PNG), audio tracks (AAC/MP3, mono/stereo), and full videos (240p–720p MP4).

The dataset and supporting scripts are hosted on GitHub: https://github.com/westlake-repl/MicroLens. Download tools include a Python script for resolving and fetching media.

## 5. Baseline Models and Benchmarking Protocol

MicroLens enables the evaluation of both collaborative filtering and content-driven recommendation approaches with the following baseline configurations:

- **IDRec (ID-only):**
  - Collaborative filtering: DSSM, LightGCN, NeuralFM, DeepFM.
  - Sequential modeling: GRU4Rec, NextItNet, SASRec.

- **VIDRec (ID plus frozen video features):**
  - Baselines: YouTube (ID), YouTube (ID+V), MMGCN, GRCN, DSSM+V, SASRec+V.
  - Video features extracted from pre-trained VideoMAE.

- **VideoRec (end-to-end video representation learning):**
  - Models: NextItNet₍V₎, GRU4Rec₍V₎, SASRec₍V₎, wherein the video encoder replaces the item embedding and is trained jointly.

Video encoders evaluated:
- **CNN-based:** R3D-r18, R3D-r50, C2D-r50, I3D-r50, CSN-r101, Slow-r50, SlowFast (r50, r101).
- **Efficient nets:** X3D (XS/S/M/L).
- **Transformer-based:** MViT-B-16×4, MViT-B-32×3, VideoMAE (ViT-B), all pre-trained on Kinetics.

Losses employ in-batch (sampled) softmax; learning rates for IDRec ∈ {1e-5, …, 1e-3}, VideoRec 1e-4 (top-block fine-tune at 1e-4); batch sizes: IDRec (64–512), VideoRec (120). AdamW optimizer with weight_decay ∈ {0, …, 0.1}, dropout=0.1.

Evaluation is conducted per test user, ranking 1 positive target among 100 negatives, with metrics at K=10 and K=20:
- Hit Ratio@K: $\mathrm{HR@K} = \mathbb{I}\bigl(\mathrm{rank}(v^*) \le K\bigr)$
- Recall@K: $\mathrm{Recall@K} = \frac{1}{|G|} \sum_{i=1}^K \mathbb{I}(r_i \in G)$
- Precision@K: $\mathrm{P@K} = \frac{1}{K}\sum_{i=1}^K \mathbb{I}(r_i\in G)$
- NDCG@K: $ \mathrm{NDCG@K} = \frac{1}{Z}\sum_{i=1}^K \frac{2^{\mathrm{rel}_i}-1}{\log_2(i+1)}$, with $\mathrm{rel}_i \in \{0,1\}$ and $Z$ as ideal DCG.

## 6. Principal Findings and Recommendations

Benchmarks reveal several modality and algorithmic trends:
- Sequential IDRec (SASRec) achieves >10% improvement over collaborative filtering IDRec.
- Adding frozen video features (VIDRec) provides negligible or negative impact in warm-item settings.
- VideoRec (end-to-end) outperforms both IDRec and VIDRec, achieving up to 2× NDCG improvement compared to frozen features.
- Pre-training on Kinetics enhances fine-tuned video encoder performance in HR@10 by ∼1.5% over random initialization; full fine-tuning yields minor additional gains.
- Top classification accuracy of video encoders (e.g., VideoMAE) does not guarantee leading recommendation accuracy.
- Optimal practice is SASRec with VideoRec: transformer backbone and joint training of top layers in the video encoder.
- User history should be limited to the most recent 13 comment interactions.
- Efficient encoding: sample 5 contiguous middle-section frames per video.
- Leave-one-out split per user best reflects sequential recommendation settings.
- Fine-tuning only the top encoder blocks suffices, mitigating catastrophic forgetting.

## 7. Prospective Extensions and Applications

MicroLens’s structure facilitates several research avenues:
- Direct modeling of audio via spectrogram encoding for improved recommendation.
- Cross-attention or advanced fusion across text, image, audio, and video for multimodal recommendation.
- Self-supervised pre-training on user–video interactions to initialize video encoders.
- Development of foundation models for video-user interaction (e.g., multimodal BERT/GPT).
- Investigation of cold-start and transfer learning for novel domains or platforms.
- Contrastive learning for alignment of user–video or video–video embeddings.

A plausible implication is that MicroLens will underpin methodological advances in scalable, multimodal, content-driven micro-video recommendation, serving as a benchmark for both current and future recommendation system architectures [2309.15379].

Source: https://www.emergentmind.com/topics/microlens-dataset