I’m trying to understand matrix factorization models for recommender systems and I always read ‘latent features’, but what does that mean? I know what a feature means for a training dataset but I’m not able to understand the idea of latent features. Every paper on the topic I can find is just too shallow.

Edit:

if you at least can point me to some papers that explain the idea.

**Answer**

Latent means not directly observable. The common use of the term in PCA and Factor Analysis is to reduce dimension of a large number of directly observable features into a smaller set of indirectly observable features.

**Attribution***Source : Link , Question Author : Jack Twain , Answer Author : samthebest*