---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: query_id
    dtype: int64
  - name: product_id
    dtype: int64
  - name: label
    dtype:
      class_label:
        names:
          '0': Irrelevant
          '1': Partial
          '2': Exact
  - name: query
    dtype: string
  - name: query_class
    dtype: string
  - name: product_name
    dtype: string
  - name: product_class
    dtype: string
  - name: category hierarchy
    dtype: string
  - name: product_description
    dtype: string
  - name: product_features
    dtype: string
  - name: rating_count
    dtype: float64
  - name: average_rating
    dtype: float64
  - name: review_count
    dtype: float64
  splits:
  - name: train
    num_bytes: 331042200.4486481
    num_examples: 140068
  - name: dev
    num_bytes: 110348975.77567595
    num_examples: 46690
  - name: test
    num_bytes: 110348975.77567595
    num_examples: 46690
  download_size: 212373125
  dataset_size: 551740152
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- sentence-similarity
- text-classification
language:
- en
size_categories:
- 100K<n<1M
---

# WANDS - Wayfair ANnotation Dataset: Dataset for product search relevance assessment

- Original source of the data is: https://github.com/wayfair/WANDS
- Train, dev, test split of 3:1:1 as per footnote 5 in https://arxiv.org/abs/2307.00370

## Details

* 42,994 candidate products
* 480 queries
* 233,448 (query,product) relevance judgements

## Column details

* Product columns:
  * product_id - ID of a product  
  * product_name - String of product name  
  * product_class - Category which product falls under  
  * category_hierarchy - Parent categories of product, delimited by ```/```  
  * product_description - String description of product  
  * product_features -  ```|``` delimited string of attribute:value pairs which describe the product  
  * rating_count - Number of user ratings for product  
  * average_rating - Average rating the product received  
  * review_count - Number of user reviews for product  
* Search queries columns:
  * query_id - unique ID for each query  
  * query - query string  
  * query_class - category to which the query falls under  
* Annotated (product,relevance judgement) pairs, columns:
  * id - Unique ID for each annotation  
  * label - Relevance label, one of 'Exact', 'Partial', or 'Irrelevant'

# Citation

Please cite this paper if you are building on top of or using this dataset:

```text
@InProceedings{wands,  
  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 = {Proceedings of the 44th European Conference on Information Retrieval},  
  year = {2022},  
  numpages = {12}  
}
```


# Code for generating dataset


```python
import pandas as pd
from datasets import Dataset
from datasets import DatasetDict, Dataset
from datasets import ClassLabel, load_from_disk, load_dataset, concatenate_datasets
from pathlib import Path

base_path = "https://github.com/wayfair/WANDS/raw/main/dataset/"

query_df = pd.read_csv(f"{base_path}/query.csv", sep='\t')
product_df = pd.read_csv(f"{base_path}/product.csv", sep='\t')
label_df = pd.read_csv(f"{base_path}/label.csv", sep='\t')

df_dataset = label_df.merge(
    query_df, on="query_id"
).merge(
    product_df, on="product_id"
)

wands_class_label_feature = ClassLabel(num_classes=3, names=["Irrelevant", "Partial", "Exact"])
dataset = dataset.train_test_split(test_size=2/5, seed=1337)
dev_test_dataset = dataset["test"].train_test_split(test_size=1/2, seed=1337)
dataset = DatasetDict(
    train=dataset["train"],
    dev=dev_test_dataset["train"],
    test=dev_test_dataset["test"],
)
"""
DatasetDict({
    train: Dataset({
        features: ['id', 'query_id', 'product_id', 'label', 'query', 'query_class', 'product_name', 'product_class', 'category hierarchy', 'product_description', 'product_features', 'rating_count', 'average_rating', 'review_count'],
        num_rows: 140068
    })
    dev: Dataset({
        features: ['id', 'query_id', 'product_id', 'label', 'query', 'query_class', 'product_name', 'product_class', 'category hierarchy', 'product_description', 'product_features', 'rating_count', 'average_rating', 'review_count'],
        num_rows: 46690
    })
    test: Dataset({
        features: ['id', 'query_id', 'product_id', 'label', 'query', 'query_class', 'product_name', 'product_class', 'category hierarchy', 'product_description', 'product_features', 'rating_count', 'average_rating', 'review_count'],
        num_rows: 46690
    })
})
"""

dataset.push_to_hub("napsternxg/wands")

```