Matrix Factorization

Matrix factorization represents users and items as vectors in a shared latent space, where each dimension captures an abstract preference factor. The user-item interaction matrix, which is mostly empty because most users interact with only a small fraction of available items, is decomposed into two smaller matrices whose product approximates the original. The missing values in this approximation become predictions.

For growth teams, matrix factorization models like SVD and ALS have been foundational recommendation techniques since Netflix Prize-era research proved their effectiveness. They scale well to large datasets and produce interpretable latent factors that can reveal meaningful preference dimensions. Modern AI has extended matrix factorization through neural collaborative filtering, which uses deep learning to capture non-linear interactions between latent factors. Growth engineers implementing recommendation systems should consider matrix factorization as a strong baseline that often outperforms more complex approaches when data is sparse. The key tuning parameters are the number of latent dimensions and regularization strength. Too few dimensions underfit, too many overfit. Teams should evaluate factorization models against simpler baselines like popularity-based recommendations to ensure the added complexity delivers genuine lift in engagement or conversion metrics.