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<p>This paper presents a content-based music recommender system that leverages deep learning to extract emotional features from audio and lyrics. Unlike traditional methods relying on popularity or user interaction data, this system uses intermediate features from a pre-trained multi-modal network to calculate emotional similarity between music tracks. The model effectively addresses the cold-start and long-tail challenges by focusing on emotional features. A new dataset of 96,309 playlists was used to evaluate the system. The proposed model significantly outperforms baseline methods and recent studies in key metrics, achieving the highest Recall and NDCG while maintaining competitive performance in Diversity and Novelty.</p>

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Content-based music recommender system based on music emotion using deep learning

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Mohammad Ali Talaghat,
  • Elham Parvinnia,
  • Reza Boostani

摘要

This paper presents a content-based music recommender system that leverages deep learning to extract emotional features from audio and lyrics. Unlike traditional methods relying on popularity or user interaction data, this system uses intermediate features from a pre-trained multi-modal network to calculate emotional similarity between music tracks. The model effectively addresses the cold-start and long-tail challenges by focusing on emotional features. A new dataset of 96,309 playlists was used to evaluate the system. The proposed model significantly outperforms baseline methods and recent studies in key metrics, achieving the highest Recall and NDCG while maintaining competitive performance in Diversity and Novelty.