  Collaborative filtering is the process of making recommendations regarding
the potential preference of a user, for example shopping on the Internet, based
on the preference ratings of the user and a number of other users for various
items. This paper considers collaborative filtering based on
explicitmulti-valued ratings. To evaluate the algorithms, weconsider only {em
pure} collaborative filtering, using ratings exclusively, and no other
information about the people or items.Our approach is to predict a user's
preferences regarding a particularitem by using other people who rated that
item and other items ratedby the user as noisy sensors. The noisy sensor model
uses Bayes' theorem to compute the probability distribution for the
user'srating of a new item. We give two variant models: in one, we learn a{em
classical normal linear regression} model of how users rate items; in
another,we assume different users rate items the same, but the accuracy of
thesensors needs to be learned. We compare these variant models
withstate-of-the-art techniques and show how they are significantly
better,whether a user has rated only two items or many. We reportempirical
results using the EachMovie database
footnote{http://research.compaq.com/SRC/eachmovie/} of movie ratings. Wealso
show that by considering items similarity along with theusers similarity, the
accuracy of the prediction increases.
