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With deep learning, we are now able to build embeddings from our data. An embedding is a vector of numbers that can represent image, text, or sound data. In the case of e-commerce, this is particularly relevant as the likeness of two products can be inferred from the similarity between their two embedding vectors. As such, this likeness is a crucial component of recommender systems. In e-commerce, the data has a taxonomy, and products are grouped in different categories and subcategories. This hierarchical structure of the data is an information in itself that can be used, but more often than not isn’t, in classifiers and machine learning systems. We will discuss common issues in e-commerce data and possible ways to alleviate some of them.