Datasets:
metadata
language:
- en
pretty_name: MemoryCD
size_categories:
- 100K<n<1M
task_categories:
- text-generation
tags:
- amazon-reviews
- cross-domain
- recommendation
- memory
- llm
- evaluation
configs:
- config_name: users_interactions
data_files:
- split: test
path: users/cross_domain_users_sampled.jsonl.gz
default: true
- config_name: meta_personal_care
data_files:
- split: test
path: meta/Beauty_and_Personal_Care.jsonl.gz
- config_name: meta_books
data_files:
- split: test
path: meta/Books.jsonl.gz
- config_name: meta_electronics
data_files:
- split: test
path: meta/Electronics.jsonl.gz
- config_name: meta_home
data_files:
- split: test
path: meta/Home_and_Kitchen.jsonl.gz
MemoryCD
Filtered cross-domain subset of Amazon Reviews 2023 for memory-augmented LLM
evaluation. All configs expose a single test split (evaluation only).
Contents
| Config | Records |
|---|---|
users_interactions |
323 users |
meta_personal_care |
33,475 items |
meta_books |
48,054 items |
meta_electronics |
25,441 items |
meta_home |
60,900 items |
The 4 meta files contain exactly the items referenced by the 323 users
(167,870 unique parent_asin, 100% coverage). The price field is
normalized to float | null across all meta files.
Quick start
from datasets import load_dataset
users = load_dataset("WZDavid/MemoryCD", "users_interactions", split="test")
books = load_dataset("WZDavid/MemoryCD", "meta_books", split="test")
Source
Derived from McAuley-Lab/Amazon-Reviews-2023.