  Recent work has shown that in a dataset of user ratings on items there exists
a group of Core Users who hold most of the information necessary for
recommendation. This set of Core Users can be as small as 20 percent of the
users. Core Users can be used to make predictions for out-of-sample users
without much additional work. Since Core Users substantially shrink a ratings
dataset without much loss of information, they can be used to improve
recommendation efficiency. We propose a method, combining latent factor models,
ensemble boosting and K-means clustering, to generate a small set of Artificial
Core Users (ACUs) from real Core User data. Our ACUs have dense rating
information, and improve the recommendation performance of real Core Users
while remaining interpretable.
