Create README.md

README.md

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+---
+license: gpl
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- n<1K
+---
+### Dataset Description
+
+This repository includes two datasets that offer an ideal ground for evaluating product matching algorithms and clustering/classification models.
+All datasets contain 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 real product comparison platforms. It has been collected by a special focused Web crawler which has been developed for
+this purpose. The first dataset was collected from PriceRunner, a popular product comparison platform. It includes 35,311 products from 10 categories,
+provided by 306 different vendors. The second dataset was acquired by crawling the products of 12 categories of Skroutz. It includes 238,170 products
+supplied by 652 electronic stores.
+
+- **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
+
+Each 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]