and LFM models)

Latent Factor Models (LFMs) utilize matrix factorization to map users and items into a shared vector space for high-precision recommendation.

LFMs resolve the data sparsity challenges inherent in massive datasets by uncovering hidden patterns (latent factors) through dimensionality reduction. These models (specifically Singular Value Decomposition) decompose a sparse user-item matrix into dense, lower-rank matrices representing distinct features. In the 2009 Netflix Prize, LFMs proved their utility by significantly reducing Root Mean Square Error (RMSE) across millions of data points. Today, engineers use libraries like Surprise or Spark MLlib to deploy these models: managing millions of parameters via Stochastic Gradient Descent (SGD) to deliver personalized content in real-time.

https://developers.google.com/machine-learning/recommendation/collaborative/matrix