Contrastive Learning-Based Music Recommendation Model

Abstract

In the rapidly evolving era of digital multimedia, the overwhelming rate of music publication poses a challenge for users seeking efficient access to their preferred songs. Music recommendation systems aim to address this issue but still encounter problems such as overfitting, the cold start problem for new users, and result bias. To tackle these challenges, we propose an optimized music recommendation model called Contrastive Learning for Music Recommendation (CLMR), leveraging contrastive learning techniques. CLMR leverages the bipartite graph information between users and songs and introduces a contrastive learning framework to enhance the representation of sparse data, thereby improving recommendation accuracy and mitigating data sparsity issues. To combat sampling bias, a comparative learning approach is employed within CLMR, utilizing Gaussian noise to construct more effective positive samples. This method enhances the model’s learning capability and robustness in challenging environments. Experimental comparisons with traditional recommendation models based on content filtering, collaborative filtering, and supervised learning demonstrate that the proposed CLMR model outperforms them, achieving superior performance in terms of NDCG and Recall metrics.