AI engineering glossary · Classical ML algorithms
What is matrix factorisation?
Matrix factorisation: Approximating a large matrix by the product of two smaller matrices whose entries are learned from the known values. It extracts hidden factors, and is widely used to predict missing entries in user-item rating tables for recommendations.
Matrix factorisation: an example
A 1 million by 50,000 rating matrix is approximated by user vectors and item vectors of length 32.
Related terms
More Classical ML algorithms terms
- Maximum margin classifier
- Mini-batch k-means
- Multicollinearity
- Multinomial naive Bayes
- Multiple linear regression
- Naive Bayes
- Non-negative matrix factorisation
- Normal equation
Where you learn it
Matrix factorisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.