Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks

• URL: http://arxiv.org/abs/2409.09026v1
• Date: Fri, 13 Sep 2024 17:53:06 GMT
• Title: Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks
• Authors: Florian Grötschla, Luca Strässle, Luca A. Lanzendörfer, Roger Wattenhofer,
• Abstract summary: Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. New music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods.
• Score: 18.95453617434051
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods. While previous approaches have relied on hand-crafted audio features for this purpose, we explore the use of contrastively pretrained neural audio embedding models, which offer a richer and more nuanced representation of music. Our experiments demonstrate that neural embeddings, particularly those generated with the Contrastive Language-Audio Pretraining (CLAP) model, present a promising approach to enhancing music recommendation tasks within graph-based frameworks.

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