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Recommender systems play an important role in many scenarios where users areoverwhelmed with too many choices to make. In this context, CollaborativeFiltering (CF) arises by providing a simple and widely used approach forpersonalized recommendation. Memory-based CF algorithms mostly rely onsimilarities between pairs of users or items, which are posteriorly employed inclassifiers like k-Nearest Neighbor (kNN) to generalize for unknown ratings. Amajor issue regarding this approach is to build the similarity matrix.Depending on the dimensionality of the rating matrix, the similaritycomputations may become computationally intractable. To overcome this issue, wepropose to represent users by their distances to preselected users, namelylandmarks. This procedure allows to drastically reduce the computational costassociated with the similarity matrix. We evaluated our proposal on twodistinct distinguishing databases, and the results showed our method hasconsistently and considerably outperformed eight CF algorithms (including bothmemory-based and model-based) in terms of computational performance.

Speeding up Memory-based Collaborative Filtering with Landmarks