AI engineering glossary · Time series recsys
What is latent factors?
Latent factors (also called Latent features): Hidden numeric attributes learned from data, such as tendency toward action versus romance, that describe users and items so that their match can be computed as a simple product. They are not named in advance and are found by the model.
Latent factors: an example
A user vector [0.8, -0.2] and film vector [0.9, 0.1] score 0.8*0.9 + (-0.2)*0.1 = 0.70.
Related terms
- Matrix factorisation
- Item embedding
- User embedding
- Collaborative filtering
More Time series recsys terms
- Learning to rank
- Listwise ranking
- Ljung-Box test
- Long tail
- Moving average
- Moving average model
- Multi-stage recommender pipeline
- Multi-step forecasting
Where you learn it
Latent factors is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.