  Recommender systems play an important role in many scenarios where users are
overwhelmed with too many choices to make. In this context, Collaborative
Filtering (CF) arises by providing a simple and widely used approach for
personalized recommendation. Memory-based CF algorithms mostly rely on
similarities between pairs of users or items, which are posteriorly employed in
classifiers like k-Nearest Neighbor (kNN) to generalize for unknown ratings. A
major issue regarding this approach is to build the similarity matrix.
Depending on the dimensionality of the rating matrix, the similarity
computations may become computationally intractable. To overcome this issue, we
propose to represent users by their distances to preselected users, namely
landmarks. This procedure allows to drastically reduce the computational cost
associated with the similarity matrix. We evaluated our proposal on two
distinct distinguishing databases, and the results showed our method has
consistently and considerably outperformed eight CF algorithms (including both
memory-based and model-based) in terms of computational performance.
