Cold-Start Music Recommendation Using Meta-Learning and Fuzzy Logic: A Hybrid Approach

Abstract

The cold-start problem remains one of the most significant challenges in recommendation systems, particularly in music platforms where user preferences are diverse and personalized experiences are crucial. This research presents a novel approach to address the cold-start problem in music recommendation by integrating meta-learning and fuzzy logic techniques. Using the LFM-2b dataset which contains over two billion music listening events, we develop a hybrid recommendation framework that can rapidly adapt to new users and items with minimal interaction history. The proposed model employs a meta-learning strategy to transfer knowledge from existing users to new ones by learning generalizable patterns of music preferences. This is complemented by a fuzzy preference modeling component that captures the inherent uncertainty and gradation in user preferences for music genres, artists and acoustic features. Our framework introduces a novel prototype-based architecture that identifies representative user and item prototypes through a clustering mechanism enhancing both recommendation accuracy and explainability. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in cold-start scenarios, achieving a 15.2% improvement in recommendation accuracy for new users and a 12.7% improvement for new items compared to traditional collaborative filtering methods. The results show that the integration of fuzzy logic with meta-learning provides a robust solution for cold-start music recommendation by effectively modeling the uncertainty in user preferences while transferring knowledge across similar user groups.