GroupRec: A Unified Toolkit for Reproducible and Inspectable Group Recommendation Research

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Group recommender systems (GRS) are being affected by methodological and procedural fragmentation. Methodologically, results-aggregation and profile-aggregation approaches are often studied with different datasets, baselines, and evaluation protocols. Procedurally, GRS experiments repeatedly reimplement dataset preparation, synthetic group generation, evaluation, and attribution in paper-specific code. This makes results difficult to reproduce, compare, and inspect beyond aggregate offline metrics. We present GroupRec, a unified toolkit that provides common abstractions for GRS research, including dataset loading, synthetic group generation, evaluation utilities, baselines from both methodological families, and reproducibility support. We also present GroupRec Inspector, an interactive web demo built atop GroupRec that lets users visually inspect different GRS strategies across groups with varying cohesion, adjust member importance, or inspect how individual members contribute to recommended items. Together, these tools provide a common experimental ground and an interactive front-end for understanding how methodological choices shape group recommendations.