Latent factors are features that are not supplied directly to a collaborative-filtering model but are learned from rating data during training. Each user and item is assigned a latent-factor vector, and the number of factors is chosen before training. The vectors are initialized, used to predict ratings, and updated through optimization to reduce prediction loss.