Datasets:

jamesding0302
/

memgen-annotations

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?.

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

from datasets import load_dataset

labels = load_dataset(
    "jamesding0302/memgen-annotations",
    "AmazonReviews2014-Beauty",
    split="test",
)
print(labels[0])

Merge with processed dataset

# 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"]))