Data
PriceRunner

PriceRunner

in_preparation ARFF BSD Visibility: public Uploaded 11-11-2023 by Leonidas Akritidis
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XSD does not comply. XSD errors: XML does not correspond to XSD schema. Error Element '{http://openml.org/openml}error': [facet 'maxLength'] The value has a length of '3347'; this exceeds the allowed maximum length of '1024'. on line 4 column 0. Error Element '{http://openml.org/openml}error': 'Problem validating uploaded description file: XML does not correspond to XSD schema. Error Element '{http://openml.org/openml}nominal_value': [facet 'pattern'] The value 'Apple iPhone XR (PRODUCT)RED 128GB' is not accepted by the pattern '\p{IsBasicLatin}*'. on line 997 column 0. Error Element '{http://openml.org/openml}nominal_value': 'Apple iPhone XR (PRODUCT)RED 128GB' is not a valid value of the atomic type '{http://openml.org/openml}basic_latin256'. on line 997 column 0. Error Element '{http://openml.org/openml}nominal_value': [facet 'pattern'] The value 'Apple iPhone XR (PRODUCT)RED 64GB' is not accepted by the pattern '\p{IsBasicLatin}*'. on line 998 column 0. Err
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This dataset originates from PriceRunner, a popular product comparison platform. It contains product-related information including product IDs, titles, and categories. It can be used for numerous tasks, such as classification, clustering, record linkage, etc. Column description: * Product ID * Product Title as it appears in the respective product comparison platform (lower case and with punctuation removed) * Vendor ID: the ID of the electronic store that provides the product. * Cluster ID: the ID of the cluster that the product belongs to. Useful for entity matching and clustering tasks. * Cluster Label: The title of the aforementioned cluster. * Category ID: the ID of the category that the product belongs to. Useful for classification and categorization tasks. * Category Label: The title of the aforementioned category. Citations: * 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. * 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. * 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 IICTAI), pp. 213-220, 2018.

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