# Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning

arXiv: https://arxiv.org/abs/2207.13909 · HTML: https://mlsanity.com/paper/2207.13909/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [musicnn: Pre-trained convolutional neural networks for music audio tagging](https://mlsanity.com/paper/1909.06654/) (2019)
- [Convolutional Recurrent Neural Networks for Music Classification](https://mlsanity.com/paper/1609.04243/) (2016)
- [Content-driven Music Recommendation: Evolution, State of the Art, and Challenges](https://mlsanity.com/paper/2107.11803/) (2021)
- [End-to-end Music Remastering System Using Self-supervised and Adversarial Training](https://mlsanity.com/paper/2202.08520/) (2022)
- [Sequential Skip Prediction with Few-shot in Streamed Music Contents](https://mlsanity.com/paper/1901.08203/) (2019)
- [The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure](https://mlsanity.com/paper/1903.06008/) (2019)
- [Skip prediction using boosting trees based on acoustic features of tracks in sessions](https://mlsanity.com/paper/1903.11833/) (2019)

## Built on by (most cited first, 4 of 4)

- [Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](https://mlsanity.com/paper/2308.12256/) (2023)
- [A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation](https://mlsanity.com/paper/2407.19886/) (2024)
- [Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users](https://mlsanity.com/paper/2403.18667/) (2024)
- [G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation](https://mlsanity.com/paper/2508.05709/) (2025)

## Abstract (arXiv)

Advanced music recommendation systems are being introduced along with the development of machine learning. However, it is essential to design a music recommendation system that can increase user satisfaction by understanding users' music tastes, not by the complexity of models. Although several studies related to music recommendation systems exploiting negative preferences have shown performance improvements, there was a lack of explanation on how they led to better recommendations. In this work, we analyze the role of negative preference in users' music tastes by comparing music recommendation models with contrastive learning exploiting preference (CLEP) but with three different training strategies - exploiting preferences of both positive and negative (CLEP-PN), positive only (CLEP-P), and negative only (CLEP-N). We evaluate the effectiveness of the negative preference by validating each system with a small amount of personalized data obtained via survey and further illuminate the possibility of exploiting negative preference in music recommendations. Our experimental results show that CLEP-N outperforms the other two in accuracy and false positive rate. Furthermore, the proposed training strategies produced a consistent tendency regardless of different types of front-end musical feature extractors, proving the stability of the proposed method.