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- W2980918163 abstract "Markup languages such as RDFa and Microdata have been widely used by e-shops to embed structured product data, as evidence has shown that they improve click-through rates for e-shops and potentially increases their sales. While e-shops often embed certain categorisation information in their product data in order to improve their products’ visibility to product search and aggregator services, such site-specific product category labels are highly inconsistent and unusable across websites. This work studies the task of automatically classifying products into a universal categorisation taxonomy, using their markup data published on the Web. Using three new neural network models adapted based on previous work, we analyse the effect of different kinds of product markup data on this task, and show that: (1) despite the highly heterogeneous nature of the site-specific categories, they can be used as very effective features - even only by themselves - for the classification task; and (2) our best performing model can significantly improve state of the art on this task by up to 9.6% points in macro-average F1." @default.
- W2980918163 created "2019-10-25" @default.
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- W2980918163 date "2019-01-01" @default.
- W2980918163 modified "2023-09-27" @default.
- W2980918163 title "Product Classification Using Microdata Annotations" @default.
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- W2980918163 doi "https://doi.org/10.1007/978-3-030-30793-6_41" @default.
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