PriceRunner
OpenML dataset with id 45714
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22117225/PriceRunner.arff
Upload date: 2 January 2024
Dataset Characteristics
Number of classes: 10
Number of features: 6 (numeric: 3, symbolic: 2 and in total binary: 0 )
Number of instances: 35,300
Number of instances with missing values: 0
Number of missing values: 0
These datasets originate from PriceRunner, a popular product comparison platform. They contain 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.
This page was built for dataset: PriceRunner
