  Recommender systems are crucial tools to overcome the information overload
brought about by the Internet. Rigorous tests are needed to establish to what
extent sophisticated methods can improve the quality of the predictions. Here
we analyse a refined correlation-based collaborative filtering algorithm and
compare it with a novel spectral method for recommending. We test them on two
databases that bear different statistical properties (MovieLens and Jester)
without filtering out the less active users and ordering the opinions in time,
whenever possible. We find that, when the distribution of user-user
correlations is narrow, simple averages work nearly as well as advanced
methods. Recommender systems can, on the other hand, exploit a great deal of
additional information in systems where external influence is negligible and
peoples' tastes emerge entirely. These findings are validated by simulations
with artificially generated data.
