A Case For Embeddings in Recommendation Problems [Arnab Bhadury]

Once you have worked on different machine learning problems, most things in the field start to feel very similar. You take your raw input data, map it to a different latent space with fewer dimensions, and then perform your classification/regression/clustering. Recommender systems, new and old, are no different. In the classic collaborative filtering problem, you factorize your partially filled usage matrix to learn user-factors and item-factors, and try to predict user ratings with a dot-product of the factors.


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