README.md

metadata

dataset_info:
  - config_name: products
    features:
      - name: product_id
        dtype: string
      - name: product_title
        dtype: string
      - name: product_description
        dtype: string
      - name: product_bullet_point
        dtype: string
      - name: product_brand
        dtype: string
      - name: product_color
        dtype: string
      - name: product_locale
        dtype: string
      - name: split
        dtype: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: train
        num_bytes: 1650407845
        num_examples: 1371823
      - name: test
        num_bytes: 537176847
        num_examples: 443101
    download_size: 1149707182
    dataset_size: 2187584692
  - config_name: queries
    features:
      - name: example_id
        dtype: int64
      - name: query
        dtype: string
      - name: query_id
        dtype: int64
      - name: product_id
        dtype: string
      - name: product_locale
        dtype: string
      - name: esci_label
        dtype: string
      - name: small_version
        dtype: int64
      - name: large_version
        dtype: int64
      - name: split
        dtype: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: train
        num_bytes: 198670365
        num_examples: 1983272
      - name: test
        num_bytes: 63544917
        num_examples: 638016
    download_size: 63596052
    dataset_size: 262215282
  - config_name: sources
    features:
      - name: query_id
        dtype: int64
      - name: source
        dtype: string
      - name: split
        dtype: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: train
        num_bytes: 3458419
        num_examples: 99683
      - name: test
        num_bytes: 1048200
        num_examples: 30969
    download_size: 1510331
    dataset_size: 4506619
configs:
  - config_name: products
    data_files:
      - split: train
        path: products/train-*
      - split: test
        path: products/test-*
  - config_name: queries
    data_files:
      - split: train
        path: queries/train-*
      - split: test
        path: queries/test-*
  - config_name: sources
    data_files:
      - split: train
        path: sources/train-*
      - split: test
        path: sources/test-*
license: apache-2.0
task_categories:
  - text-classification
  - token-classification
  - text-generation
  - text2text-generation
  - sentence-similarity
language:
  - en
  - ja
  - es
tags:
  - amazon
  - retrieval
  - search
  - ecommerce
  - ranking
  - reranking
size_categories:
  - 1M<n<10M

Amazon Shopping Queries Dataset

A comprehensive dataset for improving product search, ranking and recommendations, featuring query-product pairs with detailed relevance labels.

Overview

The dataset contains search queries paired with up to 40 potentially relevant products, each labeled using the ESCI system:

• Exact match: Products that perfectly match the customer's search intent (e.g., searching "iPhone 13" and finding "Apple iPhone 13 128GB")
• Substitute product: Alternative products that could satisfy the same need (e.g., searching "iPhone 13" and finding "iPhone 14" or "Samsung Galaxy S23")
• Complement product: Products commonly bought together with the searched item (e.g., searching "iPhone 13" and finding "iPhone 13 case" or "screen protector")
• Irrelevant result: Products that don't match the customer's search intent (e.g., searching "iPhone 13" and finding "laptop charger")

Dataset Statistics

Reduced Version (Task 1)

• 48,300 unique queries
• 1,118,011 query-product pairs
• Focus: Filtered to exclude "easy" queries, making it more challenging
• Language distribution:
  • English (US): 29,844 queries
  • Spanish (ES): 8,049 queries
  • Japanese (JP): 10,407 queries

Full Version (Tasks 2 & 3)

• 130,652 unique queries
• 2,621,738 query-product pairs
• Focus: Includes both easy and challenging queries
• Language distribution:
  • English (US): 97,345 queries
  • Spanish (ES): 15,180 queries
  • Japanese (JP): 18,127 queries

Features

• Rich product metadata including:
  • Product title
  • Product description
  • Product bullet points
  • Brand information
  • Color information
• Multilingual support (English, Japanese, Spanish)
• Train/test splits for each task

Download

Install datasets library:

pip install datasets

Donwload files:

from datasets import load_dataset

queries = load_dataset(path="Studeni/amazon-esci-data", name="queries", split=["train", "test"])
products = load_dataset(path="Studeni/amazon-esci-data", name="products", split=["train", "test"])
sources = load_dataset(path="Studeni/amazon-esci-data", name="sources", split=["train", "test"])

Use Cases

1. Product Ranking: Develop algorithms to rank relevant products higher in search results
2. Relevance Classification: Build models to classify products as Exact, Substitute, Complement, or Irrelevant
3. Substitute Detection: Identify substitute products for improved product recommendations
4. Semantic Search: Train embedding models (like BERT, sentence-transformers) to:
   • Capture semantic similarity between queries and products
   • Handle long-tail queries with no exact keyword matches
   • Understand product relationships across categories
   • Example: Query "comfortable running shoes for marathon" can match with "Nike Air Zoom Alphafly" even without exact keyword overlap

Citation

Originally sourced from "Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search", this version is optimized for machine learning applications and semantic search research.

@article{reddy2022shopping,
title={Shopping Queries Dataset: A Large-Scale {ESCI} Benchmark for Improving Product Search},
author={Chandan K. Reddy and Lluís Màrquez and Fran Valero and Nikhil Rao and Hugo Zaragoza and Sambaran Bandyopadhyay and Arnab Biswas and Anlu Xing and Karthik Subbian},
year={2022},
eprint={2206.06588},
archivePrefix={arXiv}
}