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.

翻译：随着会议投稿数量的持续增长，如何准确推荐合适的审稿人已成为一项挑战。现有方法大多遵循"论文对论文"的匹配范式，通过审稿人的发表记录来隐含地表征其专业能力。然而，有效的审稿人匹配需要捕捉多维度的专业知识，仅凭与历史论文的文本相似度往往不够。为填补这一空白，我们提出P2R——一个无需训练的框架，将隐含的论文对论文匹配转变为显式的画像匹配。P2R利用通用大语言模型为投稿论文和审稿人构建结构化画像，将其分解为主题、方法论和应用三个维度。基于这些画像，P2R采用由粗到精的流水线以平衡效率与深度：首先进行结合语义信号与层面信号的混合检索，构建高召回率的候选池；随后运用基于大语言模型的评审委员会，在严格评分量规下对候选者进行评估，整合多维度的专家视角与全面的领域主席观点。在NeurIPS、SIGIR和SciRepEval上的实验表明，P2R始终优于最先进的基线方法。消融研究进一步验证了各组件的必要性。总体而言，P2R彰显了显式结构化专业知识建模的价值，并为大语言模型应用于审稿人匹配提供了实践指导。