  Collaborative filtering is a rapidly advancing research area. Every year
several new techniques are proposed and yet it is not clear which of the
techniques work best and under what conditions. In this paper we conduct a
study comparing several collaborative filtering techniques -- both classic and
recent state-of-the-art -- in a variety of experimental contexts. Specifically,
we report conclusions controlling for number of items, number of users,
sparsity level, performance criteria, and computational complexity. Our
conclusions identify what algorithms work well and in what conditions, and
contribute to both industrial deployment collaborative filtering algorithms and
to the research community.
