ReviewHQ: An API-Based System for Reviewer Assignment and Quality Control in Research Conferences

Managing the review process for large-scale academic conferences poses significant challenges in effectively matching papers to the right reviewers, detecting conflicts of interest, ensuring review quality, and addressing potential ethical issues such as dual submissions. In this demonstration paper, we introduce ReviewHQ, an API-based system designed to streamline conference management. For matching reviewers to publications, ReviewHQ constructs expertise profiles for potential reviewers by mining their publication records and representing both papers and reviewers as dense vectors. It then formulates paper assignment as a constrained optimization problem, leveraging dense vector similarity scores and other reviewer's and submission's features to produce high-quality assignments. Beyond its core functionality, ReviewHQ identifies conflicts of interest by analyzing co-authorship histories and flags suspected dual submissions by comparing manuscripts against submission information from other conferences. Further, the system employs methods to detect low-quality or automated (AI-generated) reviews and pinpoint discrepancies between reviewers' recommendations and final acceptance decisions. ReviewHQ has been used since 2024 across the SIGIR and SIGIR-AP conferences.