LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering

TL;DR

This paper explores the use of Large Language Models combined with intelligent agents to improve personalized music recommendations, showing higher user satisfaction compared to traditional content-based methods.

Contribution

It introduces a novel multi-agent system utilizing LLMs for music recommendation and compares its performance with classical content-based filtering.

Findings

LLMs achieved satisfaction rates of up to 89.32%

The system demonstrated improved user satisfaction over traditional methods

LLMs showed promising potential in personalized music recommendation

Abstract

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.