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README.md

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 ---
 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)

build.py

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+import argparse
+import polars as pl
+import os
+
+MAPPING = {"Exact": 2, "Partial": 1, "Irrelevant": 0}
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--source", type=str, required=True, help="path to WANDS dataset"
+    )
+    args = parser.parse_args()
+
+    label = pl.read_csv(os.path.join(args.source, "label.csv"), separator="\t")
+    product = pl.read_csv(os.path.join(args.source, "product.csv"), separator="\t")
+    query = pl.read_csv(os.path.join(args.source, "query.csv"), separator="\t")
+
+    merged = label.join(product, on=pl.col("product_id"))
+    merged = merged.join(query, on=pl.col("query_id"))
+    merged = merged.with_columns(label=pl.col("label").replace(MAPPING).cast(pl.Int32))
+    print(merged.describe())
+
+    merged.write_ndjson("data.jsonl")

data/data.jsonl.gz

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