Most recommendation systems use a measure called cosine similarity, which seems to work well in practice. Last year, a team of researchers used a new theoretical framework to demonstrate why, indeed, cosine similarity yields such good results. Now they are reporting that they have used their framework to construct a new recommendation algorithm that should work better than those in use today, particularly when ratings data is "sparse" -- that is, when there is little overlap between the products reviewed and the ratings assigned by different customers.