utomated collaborative filtering is quickly becoming a
popular technique for reducing information overload, often as a technique to
complement content-based information filtering systems. In this paper we
present an algorithmic framework for performing collaborative filtering and new
algorithmic elements that increase the accuracy of collaborative prediction
algorithms. We then present a set of recommendations on selection of the right
collaborative filtering algorithmic components.