Modified matrix factorization of content-based model for recommendation system

A system, comprising:

• one or more processors;
• a memory storing computer-readable instructions which, when executed by the one or more processors, perform a method for generating recommendations, the method comprising the steps of
• receiving telemetry data captured by a population of devices, the telemetry data representing behavior of respective users of the devices with respect to features of items in a collection,
• representing the captured telemetry data in a features-as-users matrix,
• factorizing the features-as-users matrix to generate first and second low rank latent space matrices, the first low rank latent space matrix representing the features and the second low rank latent space matrix representing a content-based item model,
• building a user model by factorizing a usage model with the content-based item model, and
• generating an item-to-item recommendation utilizing the content-based item model.

A method for generating recommendations for items in collection electronically accessible by users of respective computing devices, the method comprising the steps of:

• representing item features in a matrix as if the features are users in a user-to-item usage matrix;
• building a content-based item model in which items are embedded in a low dimension latent space by factorizing the matrix;
• and
• generating recommendations by training users with the latent space.