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arXiv · 2507.18800

Semantic IDs for Music Recommendation

M. Jeffrey Mei·Florian Henkel·Samuel E. Sandberg·Oliver Bembom·Andreas F. Ehmann
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Abstract

Training recommender systems for next-item recommendation often requires unique embeddings to be learned for each item, which may take up most of the trainable parameters for a model. Shared embeddings, such as using content information, can reduce the number of distinct embeddings to be stored in memory. This allows for a more lightweight model; correspondingly, model complexity can be increased due to having fewer embeddings to store in memory. We show the benefit of using shared content-based features ('semantic IDs') in improving recommendation accuracy and diversity, while reducing model size, for two music recommendation datasets, including an online A/B test on a music streaming service.

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M. Jeffrey Mei, Florian Henkel, Samuel E. Sandberg, Oliver Bembom, Andreas F. Ehmann. 2025-07-24. Semantic IDs for Music Recommendation. https://doi.org/10.1145/3705328.3748139

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