---
dataset_info:
  features:
  - name: main_category
    dtype: string
  - name: title
    dtype: string
  - name: average_rating
    dtype: float64
  - name: rating_number
    dtype: int64
  - name: features
    sequence: string
  - name: description
    sequence: string
  - name: price
    dtype: string
  - name: images
    struct:
    - name: hi_res
      sequence: string
    - name: large
      sequence: string
    - name: thumb
      sequence: string
    - name: variant
      sequence: string
  - name: videos
    struct:
    - name: title
      sequence: string
    - name: url
      sequence: string
    - name: user_id
      sequence: string
  - name: store
    dtype: string
  - name: categories
    sequence: string
  - name: details
    dtype: string
  - name: parent_asin
    dtype: string
  - name: bought_together
    dtype: 'null'
  - name: subtitle
    dtype: string
  - name: author
    dtype: string
  - name: reviews
    list:
    - name: asin
      dtype: string
    - name: helpful_vote
      dtype: int64
    - name: images
      list:
      - name: attachment_type
        dtype: string
      - name: large_image_url
        dtype: string
      - name: medium_image_url
        dtype: string
      - name: small_image_url
        dtype: string
    - name: parent_asin
      dtype: string
    - name: rating
      dtype: float64
    - name: text
      dtype: string
    - name: timestamp
      dtype: int64
    - name: title
      dtype: string
    - name: user_id
      dtype: string
    - name: verified_purchase
      dtype: bool
  - name: qa_pairs
    list:
    - name: answers
      list:
      - name: answer
        dtype: string
      - name: candidate
        dtype: string
      - name: label
        dtype: int64
    - name: question
      dtype: string
  - name: asin
    dtype: string
  splits:
  - name: train
    num_bytes: 596400838
    num_examples: 2980
  download_size: 317160149
  dataset_size: 596400838
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-generation
- text2text-generation
- question-answering
language:
- en
tags:
- qa
size_categories:
- 1K<n<10K
---

# Amazon Combined Dataset

E-commerce dataset that combines metadata, reviews, and sample question/answer pairs. `combined.json` contains the dataset and `user2asin.json` contains a file that maps user_id from reviews to an ASIN for capturing user preferences.

## Data Fields

| Field | Type |  Explanation |
| ----- | ---- | ----------- |
| main_category | str | Main category (i.e., domain) of the product. |
| title | str | Name of the product. |
| average_rating | float | Rating of the product shown on the product page. |
| rating_number | int | Number of ratings in the product. |
| features | list | Bullet-point format features of the product. |
| description | list | Description of the product. |
| price  | float | Price in US dollars (at time of crawling). |
| images | list |  Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image. |
| videos | list | Videos of the product including title and url. |
| store | str | Store name of the product. |
| categories | list | Hierarchical categories of the product. |
| details | dict | Product details, including materials, brand, sizes, etc. |
| asin | str | ID of the product. |
| parent_asin | str | Parent ID of the product (should be same as ASIN) |
| bought_together | list | Recommended bundles from the websites. |
| reviews | list[[Review](#for-user-reviews)] | List of User Reviews, see below. |
| qa_pairs | list[str, list[[Answers](#for-answers)]] | List with question text and list of Answers, see below. |

### For User Reviews

| Field | Type |  Explanation |
| ----- | ---- | ----------- |
| rating | float | Rating of the product (from 1.0 to 5.0). |
| title  | str | Title of the user review. |
| text   | str | Text body of the user review. |
| images | list | Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively. |
| asin  | str | ID of the product. |
| parent_asin | str | Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. <b>Please use parent ID to find product meta.</b> |
| user_id | str | ID of the reviewer |
| timestamp | int | Time of the review (unix time) |
| verified_purchase | bool | User purchase verification |
| helpful_vote | int | Helpful votes of the review |

### For Answers

| Field | Type |  Explanation |
| ----- | ---- | ----------- |
| answer | str | manually written natural-sounding answer if label >= 1 |
| candidate | str | Text used to justify answer |
| label | int | 2 means fully answering, 1 means helpful but not fully answering, 0 means irrelevant |

## Datasets Used

[Amazon Reviews 2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023)
```
@article{hou2024bridging,
  title={Bridging Language and Items for Retrieval and Recommendation},
  author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian},
  journal={arXiv preprint arXiv:2403.03952},
  year={2024}
}
```
[ePQA](https://github.com/amazon-science/contextual-product-qa)
```
@article{shen2023xpqa,
  title={xPQA: Cross-Lingual Product Question Answering across 12 Languages},
  author={Shen, Xiaoyu and Asai, Akari and Byrne, Bill and de Gispert, Adri{\`a}},
  journal={arXiv preprint arXiv:2305.09249},
  year={2023}
}
```