  Automatic solutions which enable the selection of the best algorithms for a
new problem are commonly found in the literature. One research area which has
recently received considerable efforts is Collaborative Filtering. Existing
work includes several approaches using Metalearning, which relate the
characteristics of datasets with the performance of the algorithms. This work
explores an alternative approach to tackle this problem. Since, in essence,
both are recommendation problems, this work uses Collaborative Filtering
algorithms to select Collaborative Filtering algorithms. Our approach
integrates subsampling landmarkers, which are a data characterization approach
commonly used in Metalearning, with a standard Collaborative Filtering method.
The experimental results show that CF4CF competes with standard Metalearning
strategies in the problem of Collaborative Filtering algorithm selection.
