  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.
