Machine Learning with Spark - Second Edition

Basic model for Matrix Factorization

Both users and items are mapped to a joint latent factor space of dimensionality f, where user-item interaction is modeled as inner product in this space. Item i is associated with vector q where q measures the extent to which the item possesses the latent factors and User u is associated with vector p, where p measures the extent of interest the user has in the item.

The dot product  between q and p captures the interaction between user u and item I, that is, a user's interest in an item. Key to model is finding vectors q and p.

To design the model, get latent relationship between users and items. Produce ...