Encoder architectures play a pivotal role in neural news recommenders by embedding the semantic and contextual information of news and users. Thus, research has heavily focused on enhancing the representational capabilities of news and user encoders to improve recommender performance. Despite the significant impact of encoder architectures on the quality of news and user representations, existing analyses of encoder designs focus only on the overall downstream recommendation performance. This offers a one-sided assessment of the encoders' similarity, ignoring more nuanced differences in their behavior, and potentially resulting in sub-optimal model selection. In this work, we perform a comprehensive analysis of encoder architectures in neural news recommender systems. We systematically evaluate the most prominent news and user encoder architectures, focusing on their (i) representational similarity, measured with the Central Kernel Alignment, (ii) overlap of generated recommendation lists, quantified with the Jaccard similarity, and (iii) the overall recommendation performance. Our analysis reveals that the complexity of certain encoding techniques is often empirically unjustified, highlighting the potential for simpler, more efficient architectures. By isolating the effects of individual components, we provide valuable insights for researchers and practitioners to make better informed decisions about encoder selection and avoid unnecessary complexity in the design of news recommenders.

翻译：编码器架构在神经新闻推荐系统中发挥着关键作用，通过对新闻和用户的语义及上下文信息进行嵌入表示。因此，研究主要集中于增强新闻编码器和用户编码器的表征能力，以提升推荐性能。尽管编码器架构对新闻和用户表征的质量具有重要影响，现有对编码器设计的分析仅关注下游推荐的整体性能。这为编码器的相似性提供了片面的评估，忽略了其行为中更细微的差异，并可能导致次优的模型选择。在本研究中，我们对神经新闻推荐系统中的编码器架构进行了全面分析。我们系统评估了最主流的新闻和用户编码器架构，重点关注其（i）表征相似性（通过中心核对齐度量）、（ii）生成推荐列表的重叠度（通过杰卡德相似性量化）以及（iii）整体推荐性能。我们的分析表明，某些编码技术的复杂性在经验上往往缺乏依据，这突显了采用更简单、更高效架构的潜力。通过分离各组件的影响，我们为研究者和实践者提供了有价值的见解，以做出更明智的编码器选择决策，并避免在新闻推荐器设计中引入不必要的复杂性。