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.
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Notes
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This separator is chosen as it is the most commonly used in the dataset (described in Sect. 4).
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sg: http://schema.org/.
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The literature has mostly used name + other features, which we also do in this work. Also, as we shall show in the results, among n, d, and c alone, d generally performs the worst. So we do not report results based on description + other features.
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This is an assumption based on our observation, as we have been unable to confirm this with the authors despite our efforts. However, we assume this is the truth as our macro-average results are the closest, while our micro-and weighted macro-average results are significantly higher (in the range between 70 and 90).
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Zhang, Z., Paramita, M. (2019). Product Classification Using Microdata Annotations. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_41
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DOI: https://doi.org/10.1007/978-3-030-30793-6_41
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