Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignore the deeper semantic and aesthetic properties that make quotations memorable. We start from two empirical observations. First, a systematic user study shows that people consistently prefer quotations that are ``unexpected yet rational'' in context, identifying novelty as a key desideratum. Second, we find that strong existing models struggle to fully understand the deep meanings of quotations. Inspired by defamiliarization theory, we therefore formalize quote recommendation as choosing contextually novel but semantically coherent quotations. We operationalize this objective with NovelQR, a novelty-driven quotation recommendation framework. A generative label agent first interprets each quotation and its surrounding context into multi-dimensional deep-meaning labels, enabling label-enhanced retrieval. A token-level novelty estimator then reranks candidates while mitigating auto-regressive continuation bias. Experiments on bilingual datasets spanning diverse real-world domains show that our system recommends quotations that human judges rate as more appropriate, more novel, and more engaging than other baselines, while matching or surpassing existing methods in novelty estimation.

翻译：引文推荐旨在通过建议与给定语境互补的引文来丰富写作，但现有系统大多优化表面层面的主题相关性，而忽略了使引文令人难忘的深层语义与美学属性。我们从两个实证观察出发。首先，一项系统性用户研究表明，人们一致偏好那些在语境中"出乎意料却合情理"的引文，将新颖性认定为关键理想特征。其次，我们发现现有强模型难以完全理解引文的深层含义。受陌生化理论启发，我们因此将引文推荐形式化为选择语境新颖但语义连贯的引文。我们通过新颖性驱动的引文推荐框架NovelQR实现这一目标。一个生成式标签代理首先将每条引文及其周围语境解析为多维深层含义标签，实现标签增强的检索。随后，一个令牌级新颖性估计器在缓解自回归延续偏差的同时对候选进行重排序。在涵盖多样化现实世界领域的双语数据集上的实验表明，我们的系统推荐的引文被人类评判者评为比其他基线更恰当、更新颖且更具吸引力，同时在新颖性估计方面达到或超越现有方法。