  We use a binary attribute representation (BAR) model to describe a data set
of Netflix viewers' ratings of movies. We classify the viewers with discrete
bits rather than continuous parameters, which makes the representation compact
and transparent. The attributes are easy to interpret, and we need far fewer
attributes than similar methods do to achieve the same level of error. We also
take advantage of the nonuniform distribution of ratings among the movies in
the data set to train on a small selection of movies without compromising
performance on the rest of the movies.
