# Content filtering methods for music recommendation: A review

arXiv: https://arxiv.org/abs/2507.02282 · HTML: https://mlsanity.com/paper/2507.02282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [ChatMusician: Understanding and Generating Music Intrinsically with LLM](https://mlsanity.com/paper/2402.16153/) (2024)
- [Bottom-up Broadcast Neural Network For Music Genre Classification](https://mlsanity.com/paper/1901.08928/) (2019)
- [Towards Unified Music Emotion Recognition across Dimensional and Categorical Models](https://mlsanity.com/paper/2502.03979/) (2025)
- [Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model](https://mlsanity.com/paper/2208.11671/) (2022)
- [Music Recommendation System based on Emotion, Age and Ethnicity](https://mlsanity.com/paper/2212.04782/) (2022)
- [Joint sentiment analysis of lyrics and audio in music](https://mlsanity.com/paper/2405.01988/) (2024)

## Built on by (most cited first, 1 of 1)

- [Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems](https://mlsanity.com/paper/2606.13858/) (2026)

## Abstract (arXiv)

Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests content based on the preferences of users with similar listening patterns to the target user. However, this method is less effective on media where interactions are sparse. Music is one such medium, since the average user of a music streaming service will never listen to the vast majority of tracks. Due to this sparsity, there are several challenges that have to be addressed with other methods. This review examines the current state of research in addressing these challenges, with an emphasis on the role of content filtering in mitigating biases inherent in collaborative filtering approaches. We explore various methods of song classification for content filtering, including lyrical analysis using Large Language Models (LLMs) and audio signal processing techniques. Additionally, we discuss the potential conflicts between these different analysis methods and propose avenues for resolving such discrepancies.