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Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings

Understanding the contents of a large digital catalog is a significant challenge for online businesses, but this challenge can be addressed using self-supervised neural network models.

Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagging

Companies with large digital catalogs often have lots of free text data about their items, but very few actual labels, making it difficult to analyze the data and develop new features. 

Building a system that can support machine learning (ML)-powered search and discovery features while simultaneously being interpretable enough for business users to develop curated experiences is difficult.