News Recommender Systems (NRS) shape what users read, whose perspectives they encounter, and influence public discourse. Yet their design is value-laden: intentionally or not, NRS can embed undesired values in recommendation procedures, such as excluding underrepresented voices or favoring certain viewpoints, which may conflict with democratic goals. Existing solutions also lack mechanisms to explicitly control these values. Therefore, we introduce an approach that parameterizes NRS to support different democratic goals. We propose Aspect-Aware Candidate Generation (A2CG), a normatively configurable procedure for the candidate generation stage of NRS that allows designers to shape diversity in recommendations. Unlike prior work that only re-ranks candidates, A2CG introduces diversity at the start of the recommendation pipeline. A2CG represents articles along multiple diversity aspects: sentiment, political leaning, topic, and media framing. User interests are encoded using a Vector Quantized VAE, while a decoder-only model predicts the next article aspects users are likely to engage with. To broaden exposure to perspectives, A2CG injects diversity during retrieval by selectively flipping aspects in the predicted query, allowing candidate diversity to be tuned toward specific democratic models. Our method enables normative configurations that existing NRS cannot express. Unlike baselines with fixed structural biases, A2CG supports continuous calibration between democratic ideals without retraining. Empirically, A2CG generates novel, diverse, and serendipitous candidates while providing explicit parameter-driven control over the trade-off between personalization and democratic alignment. Rather than aiming for pointwise superiority, A2CG's main contribution lies in its controllability and ability to express flexible normative configurations.

翻译：新闻推荐系统（NRS）影响着用户阅读内容、接触的视角以及公众话语的走向。然而，其设计本身具有价值倾向：无论有意与否，NRS可能在推荐流程中嵌入非期望的价值取向，例如排斥弱势群体观点或偏袒特定立场，这可能与民主目标相悖。现有解决方案也缺乏显式控制这些价值取向的机制。为此，我们提出一种参数化NRS以支持不同民主目标的方法。本文提出面向方面感知的候选生成（A2CG）框架，这是一种可规范配置的流程，专用于NRS的候选生成阶段，使设计者能够塑造推荐内容的多样性。与仅对候选结果排序的先前工作不同，A2CG在推荐管线的起始阶段就引入多样性。该框架从情感倾向、政治立场、主题倾向和媒体框架四个维度对文章进行多视角表征。用户兴趣通过矢量量化变分自编码器（VQ-VAE）进行编码，而解码器模型则预测用户可能参与的下一个文章视角。为扩大视角接触面，A2CG在检索阶段通过选择性翻转预测查询中的观点维度注入多样性，从而可针对特定民主模型调节候选多样性。该方法实现了现有NRS无法表达的制度化配置。相比具有固定结构偏差的基线方法，A2CG支持在不重新训练的前提下在民主理想之间进行连续校准。实验表明，A2CG能生成新颖、多样且具有意外惊喜的候选内容，同时提供显式参数化控制机制以平衡个性化与民主对齐之间的权衡。A2CG的主要贡献不在于追求单点性能最优，而在于其可控性与表达灵活制度化配置的能力。