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Graphs are everywhere -- Psst! In Music Recommendation too

Bharani Jayakumar, Orkun Özoğlu

TL;DR

This paper tackles the challenge of improving music genre-based recommendations by capturing contextual relationships that MFCC features alone miss. It introduces graph-based embedding learning using GCN and GraphSAGE (and discusses Graph Transformer) to enrich MFCC representations, evaluated on a balanced 8-genre dataset. Empirical results show graph-augmented embeddings outperform MFCC-only baselines, with GCN achieving near-perfect accuracy in genre prediction and GraphSAGE delivering strong performance while exhibiting some overfitting in certain cases. The work highlights the practical potential of graph embeddings to deliver more accurate, personalized, and context-aware music recommendations at scale, while acknowledging computational considerations and pointing to future exploration of graph-based architectures.

Abstract

In recent years, graphs have gained prominence across various domains, especially in recommendation systems. Within the realm of music recommendation, graphs play a crucial role in enhancing genre-based recommendations by integrating Mel-Frequency Cepstral Coefficients (MFCC) with advanced graph embeddings. This study explores the efficacy of Graph Convolutional Networks (GCN), GraphSAGE, and Graph Transformer (GT) models in learning embeddings that effectively capture intricate relationships between music items and genres represented within graph structures. Through comprehensive empirical evaluations on diverse real-world music datasets, our findings consistently demonstrate that these graph-based approaches outperform traditional methods that rely solely on MFCC features or collaborative filtering techniques. Specifically, the graph-enhanced models achieve notably higher accuracy in predicting genre-specific preferences and offering relevant music suggestions to users. These results underscore the effectiveness of utilizing graph embeddings to enrich feature representations and exploit latent associations within music data, thereby illustrating their potential to advance the capabilities of personalized and context-aware music recommendation systems. Keywords: graphs, recommendation systems, neural networks, MFCC

Graphs are everywhere -- Psst! In Music Recommendation too

TL;DR

This paper tackles the challenge of improving music genre-based recommendations by capturing contextual relationships that MFCC features alone miss. It introduces graph-based embedding learning using GCN and GraphSAGE (and discusses Graph Transformer) to enrich MFCC representations, evaluated on a balanced 8-genre dataset. Empirical results show graph-augmented embeddings outperform MFCC-only baselines, with GCN achieving near-perfect accuracy in genre prediction and GraphSAGE delivering strong performance while exhibiting some overfitting in certain cases. The work highlights the practical potential of graph embeddings to deliver more accurate, personalized, and context-aware music recommendations at scale, while acknowledging computational considerations and pointing to future exploration of graph-based architectures.

Abstract

In recent years, graphs have gained prominence across various domains, especially in recommendation systems. Within the realm of music recommendation, graphs play a crucial role in enhancing genre-based recommendations by integrating Mel-Frequency Cepstral Coefficients (MFCC) with advanced graph embeddings. This study explores the efficacy of Graph Convolutional Networks (GCN), GraphSAGE, and Graph Transformer (GT) models in learning embeddings that effectively capture intricate relationships between music items and genres represented within graph structures. Through comprehensive empirical evaluations on diverse real-world music datasets, our findings consistently demonstrate that these graph-based approaches outperform traditional methods that rely solely on MFCC features or collaborative filtering techniques. Specifically, the graph-enhanced models achieve notably higher accuracy in predicting genre-specific preferences and offering relevant music suggestions to users. These results underscore the effectiveness of utilizing graph embeddings to enrich feature representations and exploit latent associations within music data, thereby illustrating their potential to advance the capabilities of personalized and context-aware music recommendation systems. Keywords: graphs, recommendation systems, neural networks, MFCC

Paper Structure

This paper contains 13 sections, 3 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Dataset Description
  5. Data Preprocessing
  6. Graph embedding learning
  7. GraphSAGE
  8. GCN
  9. Model Pipeline
  10. Experiments and Results
  11. Experimental design
  12. Algorithmic Results
  13. Conclusion

Figures (5)

  • Figure 1: Graph of Songs (20 random songs) based on Genres. The edges between the songs mean that they belong to the same genre.
  • Figure 2: Illustration of GraphSAGE - Leftside: The graph before sample and aggregation. Rightside: The neighboring nodes of the same characteristics are sampled aggregated and represented
  • Figure 3: Illustration of GCN - The Graph aka adjacency matrix is sent into the GCN layer and then the embeddings of each node are produced as output
  • Figure 4: Algorithm Pipeline - the boxed structure refers to the models employed (Graph Sample & Aggregation and Graph Convolutional Networks
  • Figure 5: Training Plot of Plain MFCC, MFCC enriched with Graph Sample and Aggregation (GraphSAGE) and Graph Convolutional Networks (GCN)