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.

如何通过使用集成方法将多个不同的最先进的算法组合起来，以在 Microsoft News 数据集（MIND）上取得卓越成果，本文旨在填补新闻推荐系统（NRS）领域中的研究空白。通过研究发现，结合多个 NRS 算法可以胜过单独算法，前提是基础学习者足够多样化，该集成方法可使结果提高 5％，同时也发现当组合不足够独特方法时不会有任何改进。这些发现为 NRS 中成功的集成方法提供了见解，并倡导通过适当的集成解决方案来开发更好的系统。