Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching

TL;DR

This paper introduces P2R, a structured, profile-based reviewer matching framework using LLMs, improving accuracy over traditional paper-to-paper methods by capturing multi-dimensional expertise.

Contribution

P2R is a novel, training-free approach that constructs explicit profiles for submissions and reviewers, enhancing matching accuracy with a coarse-to-fine pipeline.

Findings

P2R outperforms state-of-the-art baselines on multiple conference datasets.

Structured profiles improve the accuracy of reviewer matching.

Ablation studies confirm the importance of each component in P2R.

Abstract

As conference submission volumes continue to grow, accurately recommending suitable reviewers has become a challenge. Most existing methods follow a ``Paper-to-Paper'' matching paradigm, implicitly representing a reviewer by their publication history. However, effective reviewer matching requires capturing multi-dimensional expertise, and textual similarity to past papers alone is often insufficient. To address this gap, we propose P2R, a training-free framework that shifts from implicit paper-to-paper matching to explicit profile-based matching. P2R uses general-purpose LLMs to construct structured profiles for both submissions and reviewers, disentangling them into Topics, Methodologies, and Applications. Building on these profiles, P2R adopts a coarse-to-fine pipeline to balance efficiency and depth. It first performs hybrid retrieval that combines semantic and aspect-level signals to form a high-recall candidate pool, and then applies an LLM-based committee to evaluate candidates under strict rubrics, integrating both multi-dimensional expert views and a holistic Area Chair perspective. Experiments on NeurIPS, SIGIR, and SciRepEval show that P2R consistently outperforms state-of-the-art baselines. Ablation studies further verify the necessity of each component. Overall, P2R highlights the value of explicit, structured expertise modeling and offers practical guidance for applying LLMs to reviewer matching.