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

CF4CF: Recommending Collaborative Filtering algorithms using Collaborative Filtering