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

2025/08/07 by Boadana, Ronald Carvalho, Junior, Ademir Guimarães da Costa, Rios, Ricardo +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Multiagent Systems (cs.MA)

paper · doi:10.48550/arxiv.2508.11671

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 \textit89,32%, indicating their promising potential in music recommendation systems.

Citations

• Music Recommendation on Spotify using Deep Learning
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• Self-Attentive Sequential Recommendation

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