  Rating-based collaborative filtering is the process of predicting how a user
would rate a given item from other user ratings. We propose three related slope
one schemes with predictors of the form f(x) = x + b, which precompute the
average difference between the ratings of one item and another for users who
rated both. Slope one algorithms are easy to implement, efficient to query,
reasonably accurate, and they support both online queries and dynamic updates,
which makes them good candidates for real-world systems. The basic slope one
scheme is suggested as a new reference scheme for collaborative filtering. By
factoring in items that a user liked separately from items that a user
disliked, we achieve results competitive with slower memory-based schemes over
the standard benchmark EachMovie and Movielens data sets while better
fulfilling the desiderata of CF applications.
