Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild

TL;DR

This paper introduces Profiler, a lightweight module for citation recommendation that captures human citation patterns efficiently, and DAVINCI, a reranking model that achieves state-of-the-art results with an improved evaluation protocol reflecting real-world scenarios.

Contribution

The paper presents Profiler, a non-learnable citation pattern module, and DAVINCI, a new reranking model, along with an inductive evaluation protocol for more realistic assessment.

Findings

Profiler improves candidate retrieval efficiency and reduces bias.

DAVINCI achieves state-of-the-art performance on multiple benchmarks.

The new evaluation protocol better reflects real-world citation recommendation scenarios.

Abstract

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.