  We propose a new algorithm for recommender systems with numeric ratings which
is based on Pattern Structures (RAPS). As the input the algorithm takes rating
matrix, e.g., such that it contains movies rated by users. For a target user,
the algorithm returns a rated list of items (movies) based on its previous
ratings and ratings of other users. We compare the results of the proposed
algorithm in terms of precision and recall measures with Slope One, one of the
state-of-the-art item-based algorithms, on Movie Lens dataset and RAPS
demonstrates the best or comparable quality.
