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In this paper, we propose a song recommendation method for DJs that focuses on song lyrics. A DJ plays and connects a variety of songs. There are two ways to connect songs in DJ: based on musical similarities and based on semantic similarities. DJs of J-POP and anime songs tend to use connecting methods based on semantic similarity. In this paper, we focus on the lyrics of songs to support DJs of J-POP and anime songs. The task is to recommend the next song for the currently selected song. To train our model, we create a dataset from actual set lists played at DJ events. We fine-tune the Japanese pre-trained BERT in two ways: a Bi-Encoding model and a Cross-Encoding model. We experiment with Japanese pre-trained BERT as a baseline. In MRR, the Cross-Encoding model showed superior scores. In user tests, the Bi-Encoding model showed superior scores.

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Lyric-Aware DJ Track Recommendation Based on Semantic Relationships

  • 会议论文

  • 发表日期：

    2026年

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  • Wakana Kuwata,
  • Hiroaki Ohshima

摘要

In this paper, we propose a song recommendation method for DJs that focuses on song lyrics. A DJ plays and connects a variety of songs. There are two ways to connect songs in DJ: based on musical similarities and based on semantic similarities. DJs of J-POP and anime songs tend to use connecting methods based on semantic similarity. In this paper, we focus on the lyrics of songs to support DJs of J-POP and anime songs. The task is to recommend the next song for the currently selected song. To train our model, we create a dataset from actual set lists played at DJ events. We fine-tune the Japanese pre-trained BERT in two ways: a Bi-Encoding model and a Cross-Encoding model. We experiment with Japanese pre-trained BERT as a baseline. In MRR, the Cross-Encoding model showed superior scores. In user tests, the Bi-Encoding model showed superior scores.