大语言模型驱动的对话式推荐系统的用户体验：以音乐推荐为例
User Experience with LLM-powered Conversational Recommendation Systems: A Case of Music Recommendation
AI总结：
本研究通过12名参与者为期三周的音乐推荐日记研究，探究大语言模型驱动的对话式推荐系统的独特用户体验，发现其可帮助明确隐性需求、支持探索及深化音乐偏好认知，为个性化用户驱动推荐的设计提供了新方向。
摘要：
大语言模型（LLMs）的发展如今使用户能够主动与对话式推荐系统（CRS）交互，并根据自身独特需求和目标构建专属的个性化推荐服务。与传统推荐系统（RS）相比，这种体验为用户提供了显著更高的可控性，开辟了推荐体验的全新维度。基于这一背景，本研究探究了LLM驱动的CRS相较于传统RS所能提供的独特体验。通过一项为期三周、由12名参与者使用定制GPTs进行音乐推荐的日记研究，我们发现LLM驱动的CRS能够：（1）帮助用户明确隐性需求；（2）支持独特的探索过程；（3）促进用户更深入地理解自身音乐偏好。基于这些发现，我们讨论了LLM驱动的CRS所开辟的新设计空间，并强调其在支持更个性化、用户驱动的推荐体验方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
