Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations

• URL: http://arxiv.org/abs/2405.17607v1
• Date: Mon, 27 May 2024 19:12:53 GMT
• Title: Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations
• Authors: Armin Moradi, Nicola Neophytou, Golnoosh Farnadi,
• Abstract summary: Popularity bias in music recommendation systems can propagate along demographic and cultural axes. We identify these biases in recommendations for artists from underrepresented cultural groups in prototype-based matrix factorization methods. Our results demonstrate significant improvements in reducing popularity bias and enhancing demographic and cultural fairness in music recommendations.
• Score: 4.276697874428501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Popularity bias in music recommendation systems -- where artists and tracks with the highest listen counts are recommended more often -- can also propagate biases along demographic and cultural axes. In this work, we identify these biases in recommendations for artists from underrepresented cultural groups in prototype-based matrix factorization methods. Unlike traditional matrix factorization methods, prototype-based approaches are interpretable. This allows us to directly link the observed bias in recommendations for minority artists (the effect) to specific properties of the embedding space (the cause). We mitigate popularity bias in music recommendation through capturing both users' and songs' cultural nuances in the embedding space. To address these challenges while maintaining recommendation quality, we propose two novel enhancements to the embedding space: i) we propose an approach to filter-out the irrelevant prototypes used to represent each user and item to improve generalizability, and ii) we introduce regularization techniques to reinforce a more uniform distribution of prototypes within the embedding space. Our results demonstrate significant improvements in reducing popularity bias and enhancing demographic and cultural fairness in music recommendations while achieving competitive -- if not better -- overall performance.

Related papers

• ComPO: Community Preferences for Language Model Personalization [122.54846260663922]
  ComPO is a method to personalize preference optimization in language models. We collect and release ComPRed, a question answering dataset with community-level preferences from Reddit.
  arXiv  Detail & Related papers  (2024-10-21T14:02:40Z)
• Enhancing Sequential Music Recommendation with Personalized Popularity Awareness [56.972624411205224]
  This paper introduces a novel approach that incorporates personalized popularity information into sequential recommendation. Experimental results demonstrate that a Personalized Most Popular recommender outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-06T15:05:12Z)
• Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems [7.92422328367169]
  We investigate the dynamics of representation of local (i.e., country-specific) and US-produced music in user profiles and recommendations. Results suggest that most of the investigated recommendation models decrease the proportion of music from local artists in their recommendations. Users from less represented countries are, in the long term, most affected by the under-representation of their local music in recommendations.
  arXiv  Detail & Related papers  (2024-08-21T12:18:28Z)
• Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems [74.47680026838128]
  Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias. We consider multifactorial selection bias affected by both item and rating value factors. We propose smoothing and alternating gradient descent techniques to reduce variance and improve the robustness of its optimization.
  arXiv  Detail & Related papers  (2024-04-29T12:18:21Z)
• MaxMin-RLHF: Towards Equitable Alignment of Large Language Models with Diverse Human Preferences [101.57443597426374]
  Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data. We learn a mixture of preference distributions via an expectation-maximization algorithm to better represent diverse human preferences. Our algorithm achieves an average improvement of more than 16% in win-rates over conventional RLHF algorithms.
  arXiv  Detail & Related papers  (2024-02-14T03:56:27Z)
• MusicRL: Aligning Music Generation to Human Preferences [62.44903326718772]
  MusicRL is the first music generation system finetuned from human feedback. We deploy MusicLM to users and collect a substantial dataset comprising 300,000 pairwise preferences. We train MusicRL-U, the first text-to-music model that incorporates human feedback at scale.
  arXiv  Detail & Related papers  (2024-02-06T18:36:52Z)
• Popularity Degradation Bias in Local Music Recommendation [0.13597551064547497]
  We study the effect of popularity degradation bias in the context of local music recommendations. We find that both algorithms improve recommendation performance for more popular artists. Mult-VAE shows better relative performance for less popular artists.
  arXiv  Detail & Related papers  (2023-09-20T22:36:33Z)
• Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery [56.77435520571752]
  We explore the intrinsic relationship between music discovery and popularity bias. We propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations.
  arXiv  Detail & Related papers  (2023-08-28T14:12:25Z)
• Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes [10.19712238203935]
  Music preferences are strongly shaped by the cultural and socio-economic background of the listener. We use state-of-the-art unsupervised learning techniques to investigate country profiles of music preferences on the fine-grained level of music tracks. We propose a context-aware music recommendation system that leverages implicit user feedback.
  arXiv  Detail & Related papers  (2020-09-11T17:59:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.