Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks (2409.09026v1)

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.

• The paper demonstrates that contrastively pretrained CLAP embeddings significantly enhance artist relationship predictions in recommender systems.
• It integrates these embeddings within graph neural networks to effectively address the cold-start problem and outperform traditional audio features.
• The study paves the way for future research on using pretrained audio representations to advance multimedia recommendation accuracy.

Insightful Overview of the Paper on Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks

The discussion presented by Grötchla et al. revolves around a novel approach to enhancing music recommender systems through the application of contrastively pretrained neural audio embeddings. The authors specifically address the inherent limitations of traditional recommender systems, such as the cold-start problem, by integrating content-based features directly extracted from music using contrastive learning techniques. The proposed methodology incorporates Contrastive Language-Audio Pretraining (CLAP) models within graph-based frameworks, demonstrating their potential in augmenting music recommendation activities.

Research Context

In the field of music streaming services, recommender systems play a pivotal role in personalizing user experiences. This paper explores two principal methodologies—collaborative filtering and content-based approaches. The authors recognize the constraints posed by collaborative filtering due to its dependency on user interaction data, which results in the cold-start problem for new artists and tracks. In contrast, content-based models, which employ descriptive attributes derived from the musical pieces themselves, provide a promising alternative to overcome these initial limitations.

An innovative aspect of the paper is the exploration of contrastively pretrained neural models, which have gained attention for their ability to encapsulate nuanced representational data. By utilizing CLAP embeddings, the paper positions itself as a meaningful advancement in the representation of music within recommendation tasks.

Methodological Approach

The experiment employs a graph-based artist-recommendation task on an updated version of the OLGA dataset, which initially consists of artist relations from curated platforms like ALLMusic. By leveraging graph neural networks (GNNs), the paper predicts relationships between artists, particularly those previously unseen in the dataset. The neural embeddings extracted by CLAP models serve as the primary node features within the graph.

Here, the authors meticulously evaluated the utility of various features: random baseline, traditional AcousticBrainz features, Moods-Themes attributes, and CLAP embeddings—alone and in combination. GNN architectures like SAGE and GatedGCN were thoroughly assessed, with tests conducted across multiple configurations concerning the number of graph layers.

Results and Findings

The findings reveal that incorporating CLAP embeddings markedly improves the ability of models to predict artist relationships, especially when supplemented with multiple GNN layers. The results showed consistent performance enhancements compared to traditional audio features alone, highlighting CLAP embeddings as a superior methodology for capturing artist information.

While combining CLAP embeddings with other features could initially enhance performance with fewer graph layers, their impact equalizes as the layer count increases. This suggests that the significant representational capabilities of CLAP embeddings, when adequately leveraged through GNNs, can suffice independently in certain scenarios.

Implications and Future Directions

This work underscores the untapped potential of contrastively pretrained embeddings in recommender systems and suggests a robust pathway for future research in music information retrieval and recommendation frameworks. By transitioning to embeddings derived from pre-trained models like CLAP, systems can overcome historical constraints tied to handcrafted features, possibly heralding a new echelon of personalization in audio content recommendations.

The paper hints at potential future enhancements, such as revisiting model architectures for improved predictive accuracy or exploring the aggregation of multiple song features for each artist to achieve a more comprehensive representation.

In conclusion, the research by Grötchla et al. contributes substantially to music recommendation literature, providing an empirical foundation for employing advanced machine learning techniques for audio representation—an area that promises widespread applicability in various domains beyond music, including broader multimedia and multimodal recommendation systems.