  This work explores the ability of collective matrix factorization models in
recommender systems to make predictions about users and items for which there
is side information available but no feedback or interactions data, and
proposes a new formulation with a faster cold-start prediction formula that can
be used in real-time systems. While these cold-start recommendations are not as
good as warm-start ones, they were found to be of better quality than
non-personalized recommendations, and predictions about new users were found to
be more reliable than those about new items. The formulation proposed here
resulted in improved cold-start recommendations in many scenarios, at the
expense of worse warm-start ones.
