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.

翻译：新闻推荐对于促进个体获取文章至关重要，尤其是在新闻消费日益数字化的背景下。因此，大量研究致力于开发日益复杂的新闻推荐系统（NRS）算法。尽管学术研究持续深入，但在整合这些算法以产生更优结果的潜在协同效应方面，仍存在显著的研究空白。本文旨在填补这一空白，通过展示如何利用集成方法将多种多样的前沿算法相结合，在微软新闻数据集（MIND）上取得更优结果。此外，我们识别了集成方法未能提升效果的场景，并对此现象提供了解释。我们的研究结果表明，只要基学习器具备足够的多样性，NRS算法的组合能够超越单一算法，例如结合基于内容的BERT方法与协同过滤LSTUR算法的集成模型，其性能提升可达5%。同时，我们的结果也表明，在结合区分度不足的方法时，不会产生任何改进。这些发现为NRS中集成方法的成功应用提供了见解，并倡导通过恰当的集成方案开发更优的系统。