mlsanityData to 7 Oct 2026

An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music Recommendation

arXiv 2402.08300 ↗First posted 13 Feb 2024cs.CVcited by 6

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Abstract (arXiv)

Computational aesthetic evaluation has made remarkable contribution to visual art works, but its application to music is still rare. Currently, subjective evaluation is still the most effective form of evaluating artistic works. However, subjective evaluation of artistic works will consume a lot of human and material resources. The popular AI generated content (AIGC) tasks nowadays have flooded all industries, and music is no exception. While compared to music produced by humans, AI generated music still sounds mechanical, monotonous, and lacks aesthetic appeal. Due to the lack of music datasets with rating annotations, we have to choose traditional aesthetic equations to objectively measure the beauty of music. In order to improve the quality of AI music generation and further guide computer music production, synthesis, recommendation and other tasks, we use Birkhoff's aesthetic measure to design a aesthetic model, objectively measuring the aesthetic beauty of music, and form a recommendation list according to the aesthetic feeling of music. Experiments show that our objective aesthetic model and recommendation method are effective.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 28 of its 70 references that are in the map. First posted on arXiv 13 Feb 2024.

Builds on

28 of 70 references.

References · 28

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16 more
  1. 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 90
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender Systemcited by 5
  2. 2026HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendationcited by 1
  3. 2026Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendationcited by 1
  4. 2026Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Surveycited by 0
  5. 2026MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotationscited by 0
  6. 2026FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender Systemcited by 0