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In this paper, we describe a solution to tackle a common set of challenges ine-commerce, which arise from the fact that new products are continually beingadded to the catalogue. The challenges involve properly personalising thecustomer experience, forecasting demand and planning the product range. Weargue that the foundational piece to solve all of these problems is havingconsistent and detailed information about each product, information that israrely available or consistent given the multitude of suppliers and types ofproducts. We describe in detail the architecture and methodology implemented atASOS, one of the world's largest fashion e-commerce retailers, to tackle thisproblem. We then show how this quantitative understanding of the products canbe leveraged to improve recommendations in a hybrid recommender systemapproach.

Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products