用大模型智能体做音乐推荐，效果比传统方法更好。
LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
- 用大模型+智能体构建个性化推荐系统
- 用户满意度最高达89.32%
- 适合关注AI推荐与用户体验的研究者
流媒体平台音乐数量激增导致用户信息过载。为改善体验，本文研究基于Gemini和LLaMA系列大语言模型的智能体，在多智能体个性化音乐推荐系统中的应用，并与传统基于内容的推荐模型在用户满意度、新颖性和计算效率方面进行对比。实验结果显示，大模型推荐系统的用户满意度最高可达89.32%，展现出在音乐推荐领域的巨大潜力。
原文摘要 · Abstract (English)
The growing availability of music on streaming platforms has led to information overload for users. To address this issue and enhance the user experience, increasingly sophisticated recommendation systems have been proposed. This work investigates the use of Large Language Models (LLMs) from the Gemini and LLaMA families, combined with intelligent agents, in a multi-agent personalized music recommendation system. The results are compared with a traditional content-based recommendation model, considering user satisfaction, novelty, and computational efficiency. LLMs achieved satisfaction rates of up to \textit{89{,}32\%}, indicating their promising potential in music recommendation systems.
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