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Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation.

Miguel Marcos1, Lorenzo Mur-Labadia1, Ruben Martinez-Cantin1

  • 1Departamento de Informática e Ingeniería de Sistemas, Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain.

Plos One
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

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We developed Random Rotational Embedding Bayesian Optimization (ROMBO) to personalize generative deep learning models. ROMBO efficiently optimizes high-dimensional spaces, improving user satisfaction in music generation tasks.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Creativity

Background:

  • Generative deep learning models create diverse outputs via latent space sampling.
  • Personalizing these models requires efficient optimization of user preferences within the latent space.
  • Bayesian optimization is a key technique for human-in-the-loop optimization.

Purpose of the Study:

  • Introduce Random Rotational Embedding Bayesian Optimization (ROMBO) for efficient high-dimensional optimization.
  • Enable personalized content generation by optimizing user queries in generative models.
  • Evaluate ROMBO's effectiveness in a music generation task.

Main Methods:

  • Developed ROMBO, embedding low-dimensional Gaussian spaces into high-dimensional ones using random rotations.

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  • Applied ROMBO to optimize queries for a generative deep learning music model.
  • Conducted simulated experiments and user studies (n=16) for evaluation.

    • Main Results:

    • ROMBO demonstrated superior performance over baseline methods in high-dimensional optimization.
    • Achieved 16%-31% loss reduction in simulated music generation tasks.
    • User studies showed a 40% increase in finding favorite music, 16% faster discovery, and 18% less time on disliked music.

    Conclusions:

    • ROMBO offers an efficient and effective method for personalizing generative deep learning models.
    • The approach significantly enhances user experience and satisfaction in content generation tasks.
    • ROMBO shows promise for applications requiring sample-efficient optimization in high-dimensional, rotationally symmetric spaces.