Abstract
Finding out which e-shops offer a specific product is a central challenge for building integrated product catalogs and comparison shopping portals. Determining whether two offers refer to the same product involves extracting a set of features (product attributes) from the web pages containing the offers and comparing these features using a matching function. The existing gold standards for product matching have two shortcomings: (i) they only contain offers from a small number of e-shops and thus do not properly cover the heterogeneity that is found on the Web. (ii) they only provide a small number of generic product attributes and therefore cannot be used to evaluate whether detailed product attributes have been correctly extracted from textual product descriptions. To overcome these shortcomings, we have created two public gold standards: The WDC Product Feature Extraction Gold Standard consists of over 500 product web pages originating from 32 different websites on which we have annotated all product attributes (338 distinct attributes) which appear in product titles, product descriptions, as well as tables and lists. The WDC Product Matching Gold Standard consists of over \(75\,000\) correspondences between 150 products (mobile phones, TVs, and headphones) in a central catalog and offers for these products on the 32 web sites. To verify that the gold standards are challenging enough, we ran several baseline feature extraction and matching methods, resulting in F-score values in the range 0.39 to 0.67. In addition to the gold standards, we also provide a corpus consisting of 13 million product pages from the same websites which might be useful as background knowledge for training feature extraction and matching methods.
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Retail e-commerce sales worldwide from 2014 to 2019 - http://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/.
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Petrovski, P., Primpeli, A., Meusel, R., Bizer, C. (2017). The WDC Gold Standards for Product Feature Extraction and Product Matching. In: Bridge, D., Stuckenschmidt, H. (eds) E-Commerce and Web Technologies. EC-Web 2016. Lecture Notes in Business Information Processing, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-319-53676-7_6
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DOI: https://doi.org/10.1007/978-3-319-53676-7_6
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