News recommender systems are hindered by the brief lifespan of articles, as they undergo rapid relevance decay. Recent studies have demonstrated the potential of content-based neural techniques in tackling this problem. However, these models often involve complex neural architectures and often lack consideration for negative examples. In this study, we posit that the careful sampling of negative examples has a big impact on the model's outcome. We devise a negative sampling technique that not only improves the accuracy of the model but also facilitates the decentralization of the recommendation system. The experimental results obtained using the MIND dataset demonstrate that the accuracy of the method under consideration can compete with that of State-of-the-Art models. The utilization of the sampling technique is essential in reducing model complexity and accelerating the training process, while maintaining a high level of accuracy. Finally, we discuss how decentralized models can help improve privacy and scalability.

翻译：新闻推荐系统因文章生命周期短暂而受到阻碍，其相关性会迅速衰减。近期研究表明，基于内容的神经技术具有解决这一问题的潜力。然而，这些模型通常涉及复杂的神经架构，且往往缺乏对负样本的考量。本研究提出，负样本的精心采样对模型结果具有重要影响。我们设计了一种负采样技术，该技术不仅能提升模型精度，还有助于推荐系统的去中心化。使用MIND数据集获得的实验结果表明，所提方法的精度可与最先进模型相媲美。该采样技术的运用对于降低模型复杂度、加速训练过程同时保持高精度水平至关重要。最后，我们探讨了去中心化模型如何有助于提升隐私保护与可扩展性。