CF4CF: Recommending Collaborative Filtering algorithms using Collaborative Filtering

TL;DR

This paper introduces CF4CF, a novel approach that uses collaborative filtering techniques to recommend the best collaborative filtering algorithms for a given dataset, leveraging data characterization methods.

Contribution

It presents a new method that applies collaborative filtering to algorithm selection, integrating subsampling landmarkers with standard CF techniques, offering an alternative to existing metalearning approaches.

Findings

CF4CF competes with standard metalearning strategies in algorithm selection.

The approach effectively leverages data characterization for recommendation.

Experimental results validate the effectiveness of CF4CF.

Abstract

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.