Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks
<|reference_start|>Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks: 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.<|reference_end|>
@article{grötschla2024towards,
    title={Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for
  Recommender Tasks},
    author={Florian Gr"otschla, Luca Str"assle, Luca A. Lanzend"orfer, Roger
  Wattenhofer},
    journal={arXiv preprint arXiv:2409.09026},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09026},
    primaryClass={cs.SD cs.AI eess.AS}
}