Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support

TL;DR

This paper advocates for a paradigm shift in group recommender systems by integrating Generative AI to facilitate natural, chat-based human-AI interactions, enhancing decision support and practical adoption.

Contribution

It proposes leveraging modern Generative AI assistants like ChatGPT to transform group recommender systems into agentic, chat-based decision support tools, challenging traditional assumptions.

Findings

Highlights the gap between academic models and real-world needs.

Suggests AI-driven chat interactions for more natural group decision-making.

Calls for reorienting research towards AI-assisted group recommendations.

Abstract

More than twenty-five years ago, first ideas were developed on how to design a system that can provide recommendations to groups of users instead of individual users. Since then, a rich variety of algorithmic proposals were published, e.g., on how to acquire individual preferences, how to aggregate them, and how to generate recommendations for groups of users. However, despite the rich literature on the topic, barely any examples of real-world group recommender systems can be found. This lets us question common assumptions in academic research, in particular regarding communication processes in a group and how recommendation-supported decisions are made. In this essay, we argue that these common assumptions and corresponding system designs often may not match the needs or expectations of users. We thus call for a reorientation in this research area, leveraging the capabilities of modern Generative AI assistants like ChatGPT. Specifically, as one promising future direction, we envision group recommender systems to be systems where human group members interact in a chat and an AI-based group recommendation agent assists the decision-making process in an agentic way. Ultimately, this shall lead to a more natural group decision-making environment and finally to wider adoption of group recommendation systems in practice.