Boosting Simple Collaborative Filtering Models Using Ensemble Methods

TL;DR

This paper explores ensemble learning techniques to enhance collaborative filtering models, demonstrating improved prediction accuracy and computational efficiency across various base algorithms.

Contribution

It adapts popular ensemble methods for collaborative filtering, showing that ensembles of simple models can outperform single complex models in accuracy and efficiency.

Findings

Ensemble methods improve collaborative filtering prediction accuracy.

Ensembles of simple models can rival complex models in performance.

Ensembles reduce computational costs significantly.

Abstract

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learning for the collaborative filtering domain, including bagging, boosting, fusion and randomness injection. We evaluate the proposed approach on several types of collaborative filtering base models: k- NN, matrix factorization and a neighborhood matrix factorization model. Empirical evaluation shows a prediction improvement compared to all base CF algorithms. In particular, we show that the performance of an ensemble of simple (weak) CF models such as k-NN is competitive compared with a single strong CF model (such as matrix factorization) while requiring an order of magnitude less computational cost.