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

arXiv: https://arxiv.org/abs/2508.11671 · HTML: https://mlsanity.com/paper/2508.11671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Music Recommendation on Spotify using Deep Learning](https://mlsanity.com/paper/2312.10079/) (2023)

## Abstract (arXiv)

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.