After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains (2) devices chosen to implement the systems (3) prototype fidelity (4) strategies for profile transparency, justification, control, and diversity (5) strategies for group formation and final group consensus and, (6) evaluation methods applied in user studies during the design process. Therefore, we systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Systems involving artificial intelligence (AI) are protagonists in many everyday activities.
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