---
license: apache-2.0
task_categories:
- text-retrieval
- text-classification
language:
- en
tags:
- e-commerce
- search
- product-search
- relevance
- information-retrieval
size_categories:
- 100K<n<1M
pretty_name: WANDS (Wayfair ANnotation Dataset)
---

# Dataset Card for WANDS (Wayfair ANnotation Dataset)

## Dataset Summary

WANDS (Wayfair ANnotation Dataset) is the largest and richest publicly available dataset for e-commerce product search relevance. Created by Wayfair, this dataset enables objective benchmarking and evaluation of search engines in the e-commerce domain.

The dataset contains:
- **233,448** human-annotated (query, product) relevance judgments
- **42,994** candidate products with rich metadata
- **480** unique search query strings

Published as a companion to the ECIR 2022 paper "WANDS: Dataset for Product Search Relevance Assessment" by Yan Chen, Shujian Liu, Zheng Liu, Weiyi Sun, Linas Baltrunas and Benjamin Schroeder.

## Supported Tasks

- **Product Search Relevance**: Evaluate whether a product is relevant to a given search query
- **E-commerce Information Retrieval**: Train and benchmark retrieval models for product search
- **Learning-to-Rank**: Build ranking models for e-commerce search results

## Languages

The dataset is in English.

## Dataset Structure

### Data Instances

Each instance represents a query-product pair with human-annotated relevance judgment:

```json
{
  "id": 0,
  "query_id": 0,
  "product_id": 25434,
  "label": 2,
  "product_name": "21.7 '' w waiting room chair with wood frame",
  "product_class": "Waiting Room Chairs",
  "category hierarchy": "Commercial Business Furniture / Commercial Office Furniture / Office Seating / Waiting Room Chairs / Wood Waiting Room Chairs",
  "product_description": "this is a salon chair , barber chair for a hairstylist . it is cheap , classic , hydraulic pump spa equipment .",
  "product_features": "backupholsterycolor : champagne|primarymaterial : wood|...",
  "rating_count": null,
  "average_rating": null,
  "review_count": null,
  "query": "salon chair",
  "query_class": "Massage Chairs"
}
```

### Data Fields

- `id` (int): Unique identifier for the query-product pair
- `query_id` (int): Identifier for the search query
- `product_id` (int): Identifier for the product
- `label` (int): Human-annotated relevance label
  - `2`: Exact match (product is highly relevant)
  - `1`: Partial match (product is somewhat relevant)
  - `0`: Irrelevant (product is not relevant)
- `product_name` (string): Product title/name
- `product_class` (string): Product classification/type
- `category hierarchy` (string): Full category path separated by " / "
- `product_description` (string): Product description text
- `product_features` (string): Product attributes in pipe-delimited format (key:value pairs separated by "|")
- `rating_count` (int/null): Number of ratings the product has received
- `average_rating` (float/null): Average rating score
- `review_count` (int/null): Number of reviews
- `query` (string): The search query text
- `query_class` (string): Predicted product class for the query

### Data Splits

The dataset is provided as a single split containing all 233,448 annotated query-product pairs.

#### Annotation Guidelines

Relevance judgments follow three levels:
- **Exact**: Product matches the query intent precisely
- **Partial**: Product is related but not a perfect match
- **Irrelevant**: Product does not match the query intent

### Licensing Information

This dataset is released under the Apache License 2.0.

### Citation Information

```bibtex
@inproceedings{chen2022wands,
  title={WANDS: Dataset for Product Search Relevance Assessment},
  author={Chen, Yan and Liu, Shujian and Liu, Zheng and Sun, Weiyi and Baltrunas, Linas and Schroeder, Benjamin},
  booktitle={Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022},
  pages={},
  year={2022},
  organization={Springer}
}
```

## Dataset Loading

Load this dataset using the Hugging Face Datasets library:

```python
from datasets import load_dataset

dataset = load_dataset("shuttie/wands")
```

## Additional Resources

- [GitHub Repository](https://github.com/wayfair/WANDS)
- [Wayfair Tech Blog Post](https://www.aboutwayfair.com/careers/tech-blog/wayfair-releases-wands-the-largest-and-richest-publicly-available-dataset-for-e-commerce-product-search-relevance)
- [Paper on Papers with Code](https://paperswithcode.com/dataset/wands)