Pre-extracted multimodal features for MovieLens 1M, enabling research on visual, textual, and acoustic perception in recommendation systems. Derived from movie trailers and metadata, processed with state-of-the-art encoders. Released alongside our TOIS 2025 paper on fair multimodal recommendation.
Pre-extracted multimodal features for MovieLens 1M, enabling research on visual, textual, and acoustic perception in recommendation systems. Derived from movie trailers and metadata, processed with state-of-the-art encoders.
| Modality | Encoder | Dimension | Size |
|---|---|---|---|
| Visual | ResNet50 (ImageNet pre-trained) | 3,706 × 1,000 | ~30 MB |
| Textual | Sentence-Transformers | 3,706 × 384 | ~12 MB |
| Acoustic | VGGish (YouTube-8M pre-trained) | 3,706 × 128 | ~4 MB |
| Interactions | User-Item ratings with metadata | 1,000,209 records | ~39 MB |
Download Components:
Note: All features are stored in NumPy .npy format (float32).
The interaction file follows RecBole format with fields:
userID, itemID, rating, timestamp, x_label (sensitive attribute),
u_gender, u_age, u_occupation.
Total dataset size: ~85 MB. A single zip bundling all four files isn't provided yet — for now, please download
the components individually above or clone the
GitHub repo
directly.
Citation:
@article{chen2025causality,
title = {Causality-Inspired Fair Representation Learning for Multimodal Recommendation},
author = {Chen, Weixin and Chen, Li and Ni, Yongxin and Zhao, Yuhan},
journal = {ACM Transactions on Information Systems},
year = {2025},
volume = {43},
number = {6},
articleno = {153},
numpages = {29},
doi = {10.1145/3706602}
}