Abstract: Matrix factorization techniques may be employed to identify different tastes based on user history information for a user profile and to provide item recommendations for the various tastes. An item model may be generated that includes item vectors, each item vector representing an item from a catalog of items. An item vector from the item model may be identified for each of a number of items identified in information for a user profile. The item vectors may be grouped into different clusters, and a taste vector may be generated for each cluster based on item vectors in each cluster. Each taste vector may be used to select item recommendations that may be combined in a set of recommendations provided for presentation to one or more users associated with the user profile.

Abstract: In various embodiments, systems and methods are provided for enhancing media content recommendations by using feature vectors. An enhanced-matrix having a first portion and a second portion is received. The first portion of the enhanced-matrix includes a user-item matrix and the second portion of the enhanced-matrix includes a feature-item matrix. Each entry in the feature-item matrix is item metadata. An item-stem vector is determined based on a weighted sum of each of the feature vectors associated with the item. An item-latent-trait vector is generated based on the item-stem vector and an item-offset vector. The item-offset vector is an item vector for the item in the user-item matrix. One or more recommended-media content derived based on the item-latent-trait vector is provided.