milistu/amazon-esci-data

Amazon Shopping Queries Dataset

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