Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks

TL;DR

This paper explores the use of contrastively pretrained neural audio embeddings, especially CLAP, to improve music recommendation systems by addressing cold-start issues and enriching content-based information.

Contribution

It introduces the application of contrastively pretrained neural audio embeddings, like CLAP, into graph-based music recommendation frameworks, demonstrating their effectiveness.

Findings

Neural embeddings outperform traditional hand-crafted features.

CLAP embeddings significantly improve cold-start recommendations.

Contrastive pretraining enhances content-based music representations.

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.