Feature embedding in matrix factorization

One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method for enhancing media content recommendations by using feature vectors, the method comprising:

• accessing a latent space model, wherein the latent space model is associated with an enhanced-matrix;
• identifying, in the latent space model a cold item with a threshold amount of information in the enhanced-matrix;
• selecting a subset of warm items with a threshold amount of information, wherein each warm item in the subset of warm items is similar to the cold item based on features associated with the cold item;
• repositioning the cold item within the latent space model based on a vector value derived from the subset of warm items;
• and
• identifying one or more recommended-media content based on the latent space model having the cold item.

A method for enhancing media content recommendations by using feature vectors, the method comprising:

• receiving a plurality of signals, wherein the plurality of signals represent feedback for media content;
• receiving a plurality of users and items, wherein each user is associated with a plurality of features having user-metadata and each item is associated with a plurality of features having item-metadata;
• generating an enhanced-matrix having a first portion and a second portion, wherein the first portion includes a user-item matrix and the second portion includes a feature-item matrix;
• determining an item-stem vector based on a sum of each feature vector associated with the item;
• and
• generating an item-latent-trait vector based on the item-stem vector and an item-offset vector, wherein the item-offset vector is an item vector for the item in the user-item matrix;
• and
• providing one or more recommended-media content identified based on the item-latent-trait vector.