Latent Factor Models are a class of algorithms used in machine learning and statistics to uncover hidden patterns or features in data, often used in recommendation systems to predict user preferences. They achieve this by decomposing data matrices into factors that capture the underlying structure, which can be used to make predictions or identify relationships between variables.