Speeding up Memory-based Collaborative Filtering with Landmarks
This work addresses a scalability problem for recommender systems by providing a more efficient method for similarity computations, though it appears incremental as it builds on existing memory-based CF approaches.
The paper tackled the computational intractability of building similarity matrices in memory-based collaborative filtering by representing users with distances to preselected landmarks, which drastically reduced computational cost and consistently outperformed eight CF algorithms in computational performance.
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.