Previous research on group recommender systems (GRSs) has shown that group dynamics strongly influence decision-making, yet collaborative filtering (CF)–based GRSs rarely account for social interactions, partly because suitable tools to capture and analyze live interaction traces are limited. This paper introduces a community resource for studying live groups engaging with a CF-based recommender system through a domain-independent graphical interface that records structured interaction signals (e.g., suggestions, views, and favorites) and integrates them into interaction-aware consensus strategies. A live user study with 72 participants organized into 18 groups illustrates the platform’s ability to capture and analyze user interactions, including interface engagement patterns and perceived social roles. Comparing two interaction-aware consensus strategies (mean vs. completeness), we observe differences in satisfaction distributions and interaction patterns; isolating the causal contribution of interaction signals would require an interaction-unaware baseline. Source code and dataset are available online at this link ( https://github.com/davidcontrerasaguilar/GREAT.git ).