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content-based recommendations used embedding spaces for items only whereas now for collaborative filtering we are learning where users and items fit within a common embedding space along dimensions they have in common we can choose a number of dimensions represent them in either using human derived features are using latent features that are under the hood of our preferences which we will learn how to find very soon each item has a vector within this embedding space that describes the items amount of expression of each dimension each user also has a vector within this embedding space that describes how strong their preferences for each dimension for now lets keep things simple and keep things just one dimension looking at items and well get back to multi-dimensional embeddings later and how users fit in well start simple and then build ourselves up we could organize items lets say movies by similarity in one dimension for example of where they fall on the spectrum of movies for chi