| --- |
| task_categories: |
| - other |
| configs: |
| - config_name: AmazonReviews2014-Beauty |
| data_files: |
| - split: val |
| path: AmazonReviews2014-Beauty/val.jsonl |
| - split: test |
| path: AmazonReviews2014-Beauty/test.jsonl |
| - config_name: AmazonReviews2014-Sports_and_Outdoors |
| data_files: |
| - split: val |
| path: AmazonReviews2014-Sports_and_Outdoors/val.jsonl |
| - split: test |
| path: AmazonReviews2014-Sports_and_Outdoors/test.jsonl |
| - config_name: AmazonReviews2023-Industrial_and_Scientific |
| data_files: |
| - split: val |
| path: AmazonReviews2023-Industrial_and_Scientific/val.jsonl |
| - split: test |
| path: AmazonReviews2023-Industrial_and_Scientific/test.jsonl |
| - config_name: AmazonReviews2023-Musical_Instruments |
| data_files: |
| - split: val |
| path: AmazonReviews2023-Musical_Instruments/val.jsonl |
| - split: test |
| path: AmazonReviews2023-Musical_Instruments/test.jsonl |
| - config_name: AmazonReviews2023-Office_Products |
| data_files: |
| - split: val |
| path: AmazonReviews2023-Office_Products/val.jsonl |
| - split: test |
| path: AmazonReviews2023-Office_Products/test.jsonl |
| - config_name: Steam |
| data_files: |
| - split: val |
| path: Steam/val.jsonl |
| - split: test |
| path: Steam/test.jsonl |
| - config_name: Yelp-Yelp_2020 |
| data_files: |
| - split: val |
| path: Yelp-Yelp_2020/val.jsonl |
| - split: test |
| path: Yelp-Yelp_2020/test.jsonl |
| --- |
| |
| # MemGen Annotations |
|
|
| This is the annotation dataset for the paper **[How Well Does Generative Recommendation Generalize?](https://huggingface.co/papers/2603.19809)**. |
|
|
| <a href="https://huggingface.co/papers/2603.19809"><img src="https://img.shields.io/badge/Paper-ArXiv-red"></a> |
| <a href="https://github.com/Jamesding000/MemGen-GR"><img src="https://img.shields.io/badge/Code-GitHub-green"></a> |
| <a href="https://huggingface.co/jamesding0302/memgen-checkpoints"><img src="https://img.shields.io/badge/Models-Hugging%20Face-blue"></a> |
|
|
| The annotations categorize evaluation instances under the leave-one-out protocol: |
| - **test** split uses the **last** item in the user history sequence as target, |
| - **val** split uses the **second-to-last** item as target. |
|
|
| ## Columns |
| - `sample_id`: row index within the split in the original dataset. |
| - `user_id`: raw user identifier (join key). |
| - `master`: one of `memorization`, `generalization`, `uncategorized`. |
| - `subcategories`: list of `{rule, hop}` for fine-grained generalization types. |
| - `all_labels`: all string labels (e.g., `["generalization", "symmetry_3"]`). |
|
|
| ## Load in M&G annotations |
| ```python |
| from datasets import load_dataset |
| |
| labels = load_dataset( |
| "jamesding0302/memgen-annotations", |
| "AmazonReviews2014-Beauty", |
| split="test", |
| ) |
| print(labels[0]) |
| ``` |
|
|
| ## Merge with processed dataset |
| ```python |
| # 1) Load your processed dataset split (must be aligned with labels by row order) |
| ds = pipeline.split_datasets["test"] |
| |
| # 2) Append label columns to the original dataset |
| ds = (ds |
| .add_column("master", labels["master"]) |
| .add_column("subcategories", labels["subcategories"]) |
| .add_column("all_labels", labels["all_labels"])) |
| ``` |
