Modelling New Users. We generate the item ratings of a new user
from group 𝑔 by making a single draw from the multivariate Gauss-
ian distribution with mean 𝜇 (𝑔, 𝑣) and variance 𝜎 (𝑔, 𝑣)2 for each
item equal to that estimated from the training data. This has the
advantage that we can easily generate large numbers of new users
in a clean, reproducible manner. In addition, we also evaluated per-
formance when drawing ratings from the empirical distribution of
ratings for a group (so relaxing the Gaussian assumption) and also
by generating user ratings via a water-filling approach i.e. split the
data into training and test data, pick a user from the test data and
use their ratings, when we need a rating for an item that the user
has not rated, pick a second user from the same group who has
rated the item and merge the pair of user ratings. We found the
performance of these setups to be very similar to simply drawing
a new user from a Gaussian distribution.
Modelling New Users. We generate the item ratings of a new user from group 𝑔 by making a single draw from the multivariate Gauss- ian distribution with mean 𝜇 (𝑔, 𝑣) and variance 𝜎 (𝑔, 𝑣)2 for each item equal to that estimated from the training data. This has the advantage that we can easily generate large numbers of new users in a clean, reproducible manner. In addition, we also evaluated per- formance when drawing ratings from the empirical distribution of ratings for a group (so relaxing the Gaussian assumption) and also by generating user ratings via a water-filling approach i.e. split the data into training and test data, pick a user from the test data and use their ratings, when we need a rating for an item that the user has not rated, pick a second user from the same group who has rated the item and merge the pair of user ratings. We found the performance of these setups to be very similar to simply drawing a new user from a Gaussian distribution.