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Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. The authors 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.
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