AI RESEARCH

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

arXiv CS.CL

ArXi:2604.05866v1 Announce Type: cross 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