Latent Factor Models are a class of algorithms used in machine learning and statistics to uncover hidden patterns or structures within data by representing observed variables as combinations of unobserved, or latent, factors. These models are particularly effective in dimensionality reduction and recommendation systems, where they help in understanding the underlying relationships between entities, such as users and items, by capturing latent features that explain observed interactions.