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Datasets:
lakritidis
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product-matching

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
Libraries:
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pandas
License:
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metadata
license: gpl
task_categories:
  - text-classification
language:
  - en
size_categories:
  - n<1K

Dataset Description

This repository offers an ideal ground for evaluating product matching algorithms and clustering/classification models. The dataset contains e-commerce data; that is, product IDs, their titles, and their corresponding category. However, they can easily be applied to any problem which involves text/short-text mining.

The data originates from PriceRunner, a popular product comparison platform. It includes 35,311 products from 10 categories, provided by 306 different vendors. It has been collected by a special focused Web crawler which has been developed for this purpose.

  • Curated by: Leonidas Akritidis
  • Language(s) (NLP): English
  • License: GPL 2.0

Uses

Product matching, classification, clustering

Direct Use

Electronic stores, Product comparison platforms, Price comparison applications, e-Commerce systems

Dataset Structure

The CSV file comprises 7 columns:

  1. Product ID: The ID of the product
  2. Product Title: The title of the product, as it was provided by the vendor/e-shop.
  3. Vendor ID: The e-shop/vendor that sells this product.
  4. Cluster ID: All products having identical cluster IDs correspond to the same product entity. For example, the first 23 records correspond to the same product. Also useful for clustering algorithms.
  5. Cluster Label: The product title as it was provided by the product comparison platform (PRocerunner or Skroutz).
  6. Category ID: Useful for classification models. It represents the class (category) of a product.
  7. Category Label: The class label.

Citations

Researchers are kindly requested to include the following articles in their paper/s:

  1. L. Akritidis, A. Fevgas, P. Bozanis, C. Makris, "A Self-Verifying Clustering Approach to Unsupervised Matching of Product Titles", Artificial Intelligence Review (Springer), pp. 1-44, 2020.
  2. L. Akritidis, P. Bozanis, "Effective Unsupervised Matching of Product Titles with k-Combinations and Permutations", In Proceedings of the 14th IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp. 1-10, 2018.
  3. L. Akritidis, A. Fevgas, P. Bozanis, "Effective Product Categorization with Importance Scores and Morphological Analysis of the Titles", In Proceedings of the 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 213-220, 2018.

Contact

Leonidas Akritidis, [email protected]