🤖 AI Summary
To address the limitation of existing reviewer-paper matching methods in academic conferences—namely, their overreliance on a single factor leading to biased and incomplete assessments—this paper proposes a multi-factor joint modeling framework. Methodologically, it introduces the novel “factor-chain reasoning” paradigm, which decouples semantic, topical, and citation-based relevance signals into modular, composable, and interpretable components. These modules are orchestrated via instruction-tuned, context-aware language models to generate domain-agnostic scientific text embeddings, while a chain-structured architecture enables dynamic weight adjustment and progressive candidate filtering. Evaluated across four major domains—including machine learning and computer vision—the framework achieves significant improvements over state-of-the-art methods. On a newly constructed benchmark dataset, it attains a 12.7% absolute gain in mean Average Precision (mAP) and a Top-5 recall rate of 91.4%.
📝 Abstract
With the rapid increase in paper submissions to academic conferences, the need for automated and accurate paper-reviewer matching is more critical than ever. Previous efforts in this area have considered various factors to assess the relevance of a reviewer's expertise to a paper, such as the semantic similarity, shared topics, and citation connections between the paper and the reviewer's previous works. However, most of these studies focus on only one factor, resulting in an incomplete evaluation of the paper-reviewer relevance. To address this issue, we propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors. To be specific, during training, we instruction-tune a contextualized language model shared across all factors to capture their commonalities and characteristics; during inference, we chain the three factors to enable step-by-step, coarse-to-fine search for qualified reviewers given a submission. Experiments on four datasets (one of which is newly contributed by us) spanning various fields such as machine learning, computer vision, information retrieval, and data mining consistently demonstrate the effectiveness of our proposed Chain-of-Factors model in comparison with state-of-the-art paper-reviewer matching methods and scientific pre-trained language models.
