---
pretty_name: Amazon Products 2023 (Filtered)
tags:
- ecommerce
- amazon
- product
- text
- metadata
- classification
- regression
- recommendation
license: mit
task_categories:
- text-classification
- text-regression
task_ids:
- topic-classification
- rating-prediction
language:
- en
multilinguality: monolingual
source_datasets: third-party
size_categories:
- 1K<n<10K
dataset_name: aslan-ng/amazon_products_2023
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Dataset Card for **aslan-ng/amazon_products_2023**

## Dataset Summary

This dataset is a **filtered and cleaned version** of the original  
[`milistu/AMAZON-Products-2023`](https://huggingface.co/datasets/milistu/AMAZON-Products-2023) dataset.  

It contains product **titles**, **descriptions**, **main categories**, **average ratings**, and **rating counts** for a broad selection of Amazon listings from 2023, **excluding media and digital product categories** such as books, music, and software.  

The goal of this dataset is to provide a compact, cleaned corpus for **text-based regression and classification tasks**, such as predicting product categories or modeling rating patterns from product descriptions.

## Supported Tasks

- **Product Category Classification** — predict `main_category` from text.
- **Rating Regression / Quality Estimation** — predict `average_rating` or `rating_number` from `title` and `description`.
- **Representation Learning** — use product metadata for contrastive or embedding-based models.

## Languages

- English (`en`)

## Dataset Structure

### Data Instances

```json
{
  "title": "Wireless Bluetooth Earbuds, Noise Cancelling, Long Battery Life",
  "description": "True wireless earbuds with active noise cancellation, ergonomic design, and 30 hours of playtime.",
  "main_category": "meta_Electronics",
  "average_rating": 4.5,
  "rating_number": 18421,
  "product_quality_score": 4.6
}
```

### Calculation of `product_quality_score`

`product_quality_score` is computed using a **Bayesian average formula** to reduce bias toward products with few ratings.  
The score combines each product’s observed average (`R`) and the global mean (`C`) weighted by the number of votes (`v`) and a prior constant (`m`):

score = (v / (v + m)) × R + (m / (v + m)) × C

where:
- \( R \) = product’s average rating  
- \( v \) = number of ratings  
- \( C \) = mean rating across all products  
- \( m \) = minimum votes threshold (empirically chosen, e.g. 100)

The resulting score is then normalized to [0, 1] for consistency across tasks.

This approach favors well-rated products that also have **sufficient review volume**, mitigating small-sample noise.

### Data Splits

| Split | Examples |
|-------|----------:|
| train | *depends on your final filtered count (insert number if known)* |

## Curation Rationale

The source dataset (`milistu/AMAZON-Products-2023`) covers a very wide range of categories, many of which (like books, music, software, and games) are **textually biased** or **less suitable for product feature extraction tasks**.  

Therefore, this dataset intentionally **removes** those categories to focus on **tangible consumer goods** — such as electronics, home, beauty, and tools — which have more descriptive and visually grounded features.

## Data Processing

The preprocessing steps were:

1. **Start from:** `milistu/AMAZON-Products-2023`
2. **Filtered out** the following categories:
   ```python
   main_categories_to_remove = [
       "meta_Books", "meta_CDs_and_Vinyl", "meta_Digital_Music",
       "meta_Gift_Cards", "meta_Grocery_and_Gourmet_Food",
       "meta_Magazine_Subscriptions", "meta_Software", "meta_Video_Games"
   ]
   ```
3. Kept only the following columns:
   ```
   cols_to_keep = ["title", "description", "main_category", "average_rating", "rating_number"]
   ```
4. Removed rows with missing values in any of the selected columns.
5. Saved the result as the new aslan-ng/amazon_products_2023 dataset.

## Source Data
- Original dataset: milistu/AMAZON-Products-2023
- Source type: Publicly available product metadata and textual data.
- Collection year: 2023

All product texts (titles and descriptions) are originally authored by marketplace sellers or product manufacturers.

## Annotations
- Annotation process: None (directly filtered and cleaned).
- Annotators: Dataset creator (Aslan Noorghasemi).
- Label reliability: Inherits from original dataset taxonomy.

## Legal and Ethical Considerations
- Data origin: Commercially public product listings; no private data included.
- License: MIT (inherits from the original dataset license).
- Responsible use: Product texts may reflect marketing bias or linguistic exaggeration; do not use for factual analysis or high-stakes decisions.

## Limitations
- Some categories may still have subtle overlaps (e.g., meta_Office_Products vs meta_Home_and_Kitchen).
- The dataset does not include price, brand, or image data.
- Ratings are static and not timestamped.
- Small category imbalance may persist after filtering.

## Intended Uses
- Educational demonstrations of text classification or regression.
- Lightweight benchmarking for product categorization or rating prediction models.
- Feature-extraction or embedding fine-tuning tasks.

## License
- Dataset license: MIT
- Source: milistu/AMAZON-Products-2023
(please verify the source dataset’s redistribution terms before rehosting).

## Maintainers
- Maintainer: aslan-ng
- Contributions: Pull requests welcome for category balancing, multilingual extensions, or integration with structured product data.