Datasets:
File size: 1,724 Bytes
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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
```python
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](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023).
|