# Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems

arXiv: https://arxiv.org/abs/2606.13858 · HTML: https://mlsanity.com/paper/2606.13858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [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)
- [Unbiased Collaborative Filtering with Fair Sampling](https://mlsanity.com/paper/2502.13840/) (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)
- [Content filtering methods for music recommendation: A review](https://mlsanity.com/paper/2507.02282/) (2025)

## Abstract (arXiv)

Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items based on the preferences of others with similar patterns, it performs poorly in domains where user-item interactions are sparse, such as music. Content-based filtering is an alternative approach that examines the qualities of the items themselves. Genre, instrumentation, and lyrics have been explored; however, relatively little attention has been given to emotion recognition. Since a user's emotional state strongly influences their music choice, incorporating mood signals offers a promising direction for personalization. In this work, we propose a mood-conditioned ranking framework that integrates user affective signals into the recommendation process via softmax-based sampling in the energy-valence space. We evaluate the approach via single-blind experiments in which participants compare recommendations from the proposed system against a baseline. The results indicate improved perceived recommendation quality, providing preliminary evidence for the effectiveness of incorporating mood-based inputs into music recommendations.