用人类引用习惯提升论文引文推荐，更准更快无偏见。
Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild
- 设计轻量模块捕捉真实引用行为，无需训练且无偏差。
- 在多个数据集上达到新最好效果，效率与泛化性俱佳。
- 提出全新归纳式评估方式，更贴近真实论文写作场景。
恰当引用相关文献是科学成果语境化与验证的关键。现有引文推荐系统虽融合局部与全局文本信息，却常忽略人类引用行为的细微特征。近期方法虽通过引入此类模式提升性能，但计算成本高且向下游重排器引入系统性偏差。为此，我们提出 Profiler——一个轻量级、非学习型模块，可高效捕捉人类引用模式，无偏且显著提升候选检索效果。此外，我们发现当前评估协议存在关键缺陷：系统在归纳设置下评估，无法反映真实世界场景。因此，我们引入严格时间约束的归纳评估设置，模拟对新发表论文的真实引文推荐。最后，我们提出 DAVINCI，一种新型重排模型，通过自适应向量门控机制融合 Profiler 提供的置信度先验与语义信息。该系统在多个基准数据集上达到新最佳表现，证明其卓越的效率与泛化能力。
原文摘要 · Abstract (English)
Proper citation of relevant literature is essential for contextualising and validating scientific contributions. While current citation recommendation systems leverage local and global textual information, they often overlook the nuances of the human citation behaviour. Recent methods that incorporate such patterns improve performance but incur high computational costs and introduce systematic biases into downstream rerankers. To address this, we propose Profiler, a lightweight, non-learnable module that captures human citation patterns efficiently and without bias, significantly enhancing candidate retrieval. Furthermore, we identify a critical limitation in current evaluation protocol: the systems are assessed in a transductive setting, which fails to reflect real-world scenarios. We introduce a rigorous Inductive evaluation setting that enforces strict temporal constraints, simulating the recommendation of citations for newly authored papers in the wild. Finally, we present DAVINCI, a novel reranking model that integrates profiler-derived confidence priors with semantic information via an adaptive vector-gating mechanism. Our system achieves new state-of-the-art results across multiple benchmark datasets, demonstrating superior efficiency and generalisability.
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