| ---
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| license: apache-2.0
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| task_categories:
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| - text-retrieval
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| - text-classification
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| language:
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| - en
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| tags:
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| - e-commerce
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| - search
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| - product-search
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| - relevance
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| - information-retrieval
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| size_categories:
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| - 100K<n<1M
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| 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.
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|
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| The dataset contains:
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| - **233,448** human-annotated (query, product) relevance judgments
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| - **42,994** candidate products with rich metadata
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| - **480** unique search query strings
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|
|
| 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
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| - **E-commerce Information Retrieval**: Train and benchmark retrieval models for product search
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| - **Learning-to-Rank**: Build ranking models for e-commerce search results
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|
|
| ## Languages
|
|
|
| The dataset is in English.
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|
|
| ## Dataset Structure
|
|
|
| ### Data Instances
|
|
|
| Each instance represents a query-product pair with human-annotated relevance judgment:
|
|
|
| ```json
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| {
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| "id": 0,
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| "query_id": 0,
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| "product_id": 25434,
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| "label": 2,
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| "product_name": "21.7 '' w waiting room chair with wood frame",
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| "product_class": "Waiting Room Chairs",
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| "category hierarchy": "Commercial Business Furniture / Commercial Office Furniture / Office Seating / Waiting Room Chairs / Wood Waiting Room Chairs",
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| "product_description": "this is a salon chair , barber chair for a hairstylist . it is cheap , classic , hydraulic pump spa equipment .",
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| "product_features": "backupholsterycolor : champagne|primarymaterial : wood|...",
|
| "rating_count": null,
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| "average_rating": null,
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| "review_count": null,
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| "query": "salon chair",
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| "query_class": "Massage Chairs"
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| }
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| ```
|
|
|
| ### Data Fields
|
|
|
| - `id` (int): Unique identifier for the query-product pair
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| - `query_id` (int): Identifier for the search query
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| - `product_id` (int): Identifier for the product
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| - `label` (int): Human-annotated relevance label
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| - `2`: Exact match (product is highly relevant)
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| - `1`: Partial match (product is somewhat relevant)
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| - `0`: Irrelevant (product is not relevant)
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| - `product_name` (string): Product title/name
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| - `product_class` (string): Product classification/type
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| - `category hierarchy` (string): Full category path separated by " / "
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| - `product_description` (string): Product description text
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| - `product_features` (string): Product attributes in pipe-delimited format (key:value pairs separated by "|")
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| - `rating_count` (int/null): Number of ratings the product has received
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| - `average_rating` (float/null): Average rating score
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| - `review_count` (int/null): Number of reviews
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| - `query` (string): The search query text
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| - `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:
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| - **Exact**: Product matches the query intent precisely
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| - **Partial**: Product is related but not a perfect match
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| - **Irrelevant**: Product does not match the query intent
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|
|
| ### Licensing Information
|
|
|
| This dataset is released under the Apache License 2.0.
|
|
|
| ### Citation Information
|
|
|
| ```bibtex
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| @inproceedings{chen2022wands,
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| title={WANDS: Dataset for Product Search Relevance Assessment},
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| author={Chen, Yan and Liu, Shujian and Liu, Zheng and Sun, Weiyi and Baltrunas, Linas and Schroeder, Benjamin},
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| booktitle={Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022},
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| pages={},
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| year={2022},
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| organization={Springer}
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| }
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| ```
|
|
|
| ## Dataset Loading
|
|
|
| Load this dataset using the Hugging Face Datasets library:
|
|
|
| ```python
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| from datasets import load_dataset
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|
|
| dataset = load_dataset("shuttie/wands")
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| ```
|
|
|
| ## Additional Resources
|
|
|
| - [GitHub Repository](https://github.com/wayfair/WANDS)
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| - [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)
|
|
|
