User Experience with LLM-powered Conversational Recommendation Systems: A Case of Music Recommendation

Abstract

The advancement of large language models (LLMs) now allows users to actively interact with conversational recommendation systems (CRS) and build their own personalized recommendation services tailored to their unique needs and goals. This experience offers users a significantly higher level of controllability compared to traditional RS, enabling an entirely new dimension of recommendation experiences. Building on this context, this study explored the unique experiences that LLM-powered CRS can provide compared to traditional RS. Through a three-week diary study with 12 participants using custom GPTs for music recommendations, we found that LLM-powered CRS can (1) help users clarify implicit needs, (2) support unique exploration, and (3) facilitate a deeper understanding of musical preferences. Based on these findings, we discuss the new design space enabled by LLM-powered CRS and highlight its potential to support more personalized, user-driven recommendation experiences.

References in corpus (11)

• To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
• Design Principles for Generative AI Applications
• Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
• Towards Relatable Explainable AI with the Perceptual Process
• Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems
• Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking
• Machine Learning Processes as Sources of Ambiguity: Insights from AI Art
• Towards Empathetic Conversational Recommender Systems
• OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
• Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems
• Developing a Conversational Recommendation System for Navigating Limited Options