What is it about?
Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. This paper provides a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offering designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context.
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Why is it important?
We systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. 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.
Perspectives
Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts.
Oscar Alvarado
Universidad de Costa Rica
Read the Original
This page is a summary of: A Systematic Review of Interaction Design Strategies for Group Recommendation Systems, Proceedings of the ACM on Human-Computer Interaction, November 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3555161.
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