Evaluating Ensemble Methods for News Recommender Systems

TL;DR

This paper investigates how ensemble methods can enhance news recommender systems by combining diverse algorithms, demonstrating up to 5% improvements on the Microsoft News dataset and highlighting scenarios where ensemble approaches are ineffective.

Contribution

It demonstrates the potential of ensemble methods to improve news recommendation accuracy by combining diverse algorithms and identifies conditions for their success or failure.

Findings

Ensemble methods can outperform individual algorithms with sufficient diversity.

Up to 5% accuracy improvement observed with specific algorithm combinations.

Combining similar or insufficiently diverse algorithms does not improve results.

Abstract

News recommendation is crucial for facilitating individuals' access to articles, particularly amid the increasingly digital landscape of news consumption. Consequently, extensive research is dedicated to News Recommender Systems (NRS) with increasingly sophisticated algorithms. Despite this sustained scholarly inquiry, there exists a notable research gap regarding the potential synergy achievable by amalgamating these algorithms to yield superior outcomes. This paper endeavours to address this gap by demonstrating how ensemble methods can be used to combine many diverse state-of-the-art algorithms to achieve superior results on the Microsoft News dataset (MIND). Additionally, we identify scenarios where ensemble methods fail to improve results and offer explanations for this occurrence. Our findings demonstrate that a combination of NRS algorithms can outperform individual algorithms, provided that the base learners are sufficiently diverse, with improvements of up to 5\% observed for an ensemble consisting of a content-based BERT approach and the collaborative filtering LSTUR algorithm. Additionally, our results demonstrate the absence of any improvement when combining insufficiently distinct methods. These findings provide insight into successful approaches of ensemble methods in NRS and advocates for the development of better systems through appropriate ensemble solutions.