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**Description** |
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This repository includes two datasets that offer an ideal ground for evaluating product matching algorithms and clustering/classification models. |
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All datasets contain e-commerce data; that is, product IDs, their titles, and their corresponding category. However, they can easily be applied |
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to any problem which involves text/short-text mining. |
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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. |
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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. |
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The second dataset was acquired by crawling the products of 12 categories of Skroutz. It includes 238,170 products supplied by 652 electronic stores. |
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**Columns** |
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1. Product ID: The ID of the product |
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2. Product Title: The title of the product, as it was provided by the vendor/e-shop. |
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3. Vendor ID: The e-shop/vendor that sells this product. |
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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. |
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5. Cluster Label: The product title as it was provided by the product comparison platform (PRocerunner or Skroutz). |
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6. Category ID: Useful for classification models. It represents the class (category) of a product. |
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7. Category Label: The class label. |
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**Licence** |
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The datasets are licensed under General Public License (GPL 2.0). |
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**Relevant Papers** |
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Researchers are kindly requested to include the following articles in their paper/s: |
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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. |
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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. |
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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. |
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