| --- |
| pretty_name: MAGE multimodal recommendation datasets |
| language: |
| - en |
| tags: |
| - recommendation |
| - multimodal |
| - graph-learning |
| - arxiv:2610.37409 |
| configs: |
| - config_name: baby |
| default: true |
| data_files: |
| - split: train |
| path: data/baby/train.parquet |
| - split: validation |
| path: data/baby/validation.parquet |
| - split: test |
| path: data/baby/test.parquet |
| - config_name: sports |
| data_files: |
| - split: train |
| path: data/sports/train.parquet |
| - split: validation |
| path: data/sports/validation.parquet |
| - split: test |
| path: data/sports/test.parquet |
| - config_name: clothing |
| data_files: |
| - split: train |
| path: data/clothing/train.parquet |
| - split: validation |
| path: data/clothing/validation.parquet |
| - split: test |
| path: data/clothing/test.parquet |
| - config_name: elec |
| data_files: |
| - split: train |
| path: data/elec/train.parquet |
| - split: validation |
| path: data/elec/validation.parquet |
| - split: test |
| path: data/elec/test.parquet |
| --- |
| |
| # MAGE datasets |
|
|
| Prepared Baby, Sports, Clothing, and Electronics datasets for **Can Complementary Signals Bridge Similarity Islands? Manifold-Augmented Graph Embedding for Multimodal Recommendation**. |
|
|
| - [Paper](https://arxiv.org/abs/2610.37409) |
| - [Code and training instructions](https://github.com/Alex-bupt/mage) |
| - [Input format](https://github.com/Alex-bupt/mage/blob/main/docs/DATA_FORMAT.md) |
|
|
| The release contains fixed interaction splits, item and user ID mappings, visual and text feature matrices, an expanded text-feature catalog, and offline relation graphs. It provides numeric features rather than raw images or review text. These prepared Amazon-category benchmarks build on the data used by [GUME](https://github.com/Alex-bupt/mage/blob/main/docs/upstream/GUME_README.md) and MMRec. |
|
|
| ## Contents |
|
|
| `archive/MAGE_datasets_2026-10-08.zip` is an unchanged mirror of the prepared dataset archive previously distributed through Google Drive. Extract it with a ZIP64-compatible utility to obtain: |
|
|
| ```text |
| MAGE_datasets/ |
| baby/ |
| sports/ |
| clothing/ |
| elec/ |
| README.txt |
| SHA256SUMS.txt |
| ``` |
|
|
| Electronics uses `elec` as its directory and dataset name in the code. |
|
|
| | Dataset | Recommendation items | Expanded text rows | Stage-1 `--seed-max` | |
| | --- | ---: | ---: | ---: | |
| | Baby | 7,050 | 71,317 | 7,049 | |
| | Sports | 18,357 | 77,832 | 18,356 | |
| | Clothing | 23,033 | 84,695 | 23,032 | |
| | Electronics | 63,001 | 498,196 | 63,000 | |
|
|
| Each dataset directory includes: |
|
|
| | File | Contents | |
| | --- | --- | |
| | `<dataset>.inter` | Original interaction table and split labels | |
| | `<dataset>_hard_eval.inter` | Same training/validation rows, with a restricted hard test subset | |
| | `i_id_mapping.csv`, `u_id_mapping.csv` | Original catalog/user identifiers and zero-based model IDs | |
| | `image_feat.npy` | Aligned visual features, `(items, 4096)`, float64 | |
| | `text_feat.npy` | Augmented text features, `(items, 512)`, float32 | |
| | `text_feat_raw.npy` | Unmodified recommendation-item text features | |
| | `text_feat_full.npy`, `i_id_mapping_full.csv` | Expanded text catalog and aligned item mapping | |
| | `related_by_id.json` | Relations over the expanded item IDs | |
| | `p2b_map_filtered.json` | Complementary edges between recommendation items | |
| | `offline_manifest.json` | Preparation settings, shapes and feature hashes | |
|
|
| The `.inter` files and the mapping `.csv` files are **tab-separated**. Interaction columns are `userID`, `itemID`, `rating`, `timestamp`, and `x_label`. The split labels are `0` = training, `1` = validation, and `2` = test. Keep the supplied IDs, row order and splits unchanged. The user mapping retains the original benchmark's pseudonymous user identifiers. |
|
|
| ## Explore interaction splits |
|
|
| The Parquet files under `data/` expose the original interaction table as four Hugging Face configurations: `baby`, `sports`, `clothing`, and `elec`. Each retains the five source columns and separates rows according to the existing `x_label`; no split is resampled. These tables also power the dataset viewer. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("Alex6657/MAGE", "baby") |
| print(dataset["train"][0]) |
| ``` |
|
|
| | Configuration | Users | Training rows | Validation rows | Test rows | |
| | --- | ---: | ---: | ---: | ---: | |
| | `baby` | 19,445 | 118,551 | 20,559 | 21,682 | |
| | `sports` | 35,598 | 218,409 | 37,899 | 40,029 | |
| | `clothing` | 39,387 | 197,338 | 40,150 | 41,189 | |
| | `elec` | 192,403 | 1,304,382 | 192,403 | 192,403 | |
|
|
| The Parquet configurations contain interactions only. Download the archive for feature matrices, mappings, graphs and the separate hard-test files needed for the full MAGE workflow. |
|
|
| ## Download the training data |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| archive = hf_hub_download( |
| repo_id="Alex6657/MAGE", |
| repo_type="dataset", |
| filename="archive/MAGE_datasets_2026-10-08.zip", |
| ) |
| print(archive) |
| ``` |
|
|
| The archive is **1,882,496,634 bytes**. SHA-256: |
|
|
| ```text |
| 8412c252d01efbcadc48101b3d3634191318e554d11219a93119b6253fb0651a |
| ``` |
|
|
| The extracted archive contains `SHA256SUMS.txt` for its files. From `MAGE_datasets/`, verify it with `sha256sum -c SHA256SUMS.txt` where available. |
|
|
| ## Prepare text features for training |
|
|
| The archived `text_feat.npy` files use Stage-1 gap retention **0.60**. The current Baby, Sports and Clothing configurations in the code repository use **0.20**, **0.40** and **0.40**, respectively. For those configurations, regenerate `text_feat.npy` from `text_feat_full.npy` using the repository's Stage-1 commands before training. Preserve the original files and use a fresh working directory without graph caches. |
|
|
| `--seed-max` is the inclusive last recommendation item ID. Expanded text rows supply neighbor features and must not be passed directly as the Stage-2 training feature matrix. The first recommendation-item rows of the expanded mapping must match the seed mapping. All text embeddings have 512 columns; visual features have 4096 columns. |
|
|
| Electronics has 480 zero visual-feature rows, representing missing visual signal. |
|
|
| ## Use and attribution |
|
|
| These files are intended for research on multimodal recommendation. Preserve the original benchmark attribution and applicable third-party terms. This mirror does not grant additional rights to the underlying datasets. The code repository retains its [upstream references and notices](https://github.com/Alex-bupt/mage/blob/main/docs/upstream/GUME_README.md). |
|
|
| Please cite the [MAGE paper](https://arxiv.org/abs/2610.37409) when using the prepared features and graphs, together with the relevant original data and method sources. |
|
|
