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.

翻译：正确引用相关文献对于科学贡献的语境化和验证至关重要。虽然现有的引文推荐系统利用局部和全局文本信息，但它们往往忽视了人类引用行为的细微差别。近期融入此类模式的方法虽然提升了性能，但带来了高昂的计算成本，并向下游重排序器引入了系统性偏差。为解决这些问题，我们提出Profiler，一种轻量级、不可学习的模块，能够高效且无偏地捕捉人类引用模式，显著增强候选检索。此外，我们识别出当前评估协议的一个关键局限性：系统在直推式设置下进行评估，这无法反映真实世界场景。我们引入一种严格的归纳式评估设置，施加严格的时间约束，模拟对新撰写论文在野外环境中的引文推荐。最后，我们提出DAVINCI，一种新颖的重排序模型，通过自适应向量门控机制将来自Profiler的置信度先验与语义信息相结合。我们的系统在多个基准数据集上取得了新的最佳成果，展现了卓越的高效性和泛化能力。