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

TL;DR

This study explores how LLM-powered conversational recommendation systems enhance user experience in music recommendation by enabling better need clarification, exploration, and understanding of preferences, compared to traditional systems.

Contribution

It provides empirical insights into the unique user experiences enabled by LLM-powered CRS in music recommendation, highlighting new design opportunities.

Findings

LLM-powered CRS helps clarify implicit user needs.

Supports unique exploration of music preferences.

Facilitates deeper understanding of musical tastes.

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.