https://github.com/eva-kaushik/emkgcn-multimodal-music-recommender

The `MKGCN` class, coupled with the Spotify API, orchestrates a multi-modal knowledge graph convolutional network to enhance music recommendation systems by integrating user interaction data and diverse music modalities.
https://github.com/eva-kaushik/emkgcn-multimodal-music-recommender

emkgcn multimodal-deep-learning music-recommendation-system spotify-api spotipy-library

Last synced: 4 months ago
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The `MKGCN` class, coupled with the Spotify API, orchestrates a multi-modal knowledge graph convolutional network to enhance music recommendation systems by integrating user interaction data and diverse music modalities.

• Host: GitHub
• URL: https://github.com/eva-kaushik/emkgcn-multimodal-music-recommender
• Owner: Eva-Kaushik
• Created: 2024-04-16T07:34:40.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-30T17:32:55.000Z (about 1 year ago)
• Last Synced: 2024-12-22T18:44:38.504Z (6 months ago)
• Topics: emkgcn, multimodal-deep-learning, music-recommendation-system, spotify-api, spotipy-library
• Language: Jupyter Notebook
• Homepage:
• Size: 11 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Multi-modal Knowledge Graph Convolutional Networks for Music Recommender System 

# Emotion based Multimodal Music Recommendation System

Implements a state-of-the-art multimodal music recommendation system using the Multimodal Knowledge Graph Convolutional Network (MKGCN) architecture.

## Features

- **Multimodal Integration**: Utilizes multiple data modalities to capture rich information about songs.

- **Advanced Recommendation Algorithm**: Employs the MKGCN architecture, which combines graph convolutional networks with multimodal embeddings to generate accurate and diverse recommendations.

- **Interactive Interface**: Provides a user-friendly web interface powered by PyQt5, allowing users to input their preferences and receive personalized song recommendations in real-time.

# Sequence to run the files

1. For Multimodal-Knowledge Graph Convolutional Network 

- Download this complete project and open in Visual Studio Code

- Connect to venv3.11 (Use Command: cd path_to_your_directory then venv3.11\Scripts\activate)

- Run Configure_Data.ipynb 

- Utils.ipynb 

- EMKGCN Data Loader.ipynb

- Multimodal_aggregator

- Neighbor_Aggregator

- Modal.ipynb (Make sure here you provide your system file path for (modal_0.tsv, modal_1.tsv....)

- EMKGCN Main (Change the Model Directory Path based on your system file path)

2. Connecting with Spotify API for most updated results

- Training_Spotipy

- Spotipy.ipynb (This is will playlist for 7 specified emotions)

- PyQt5.ipynb (Do use the paths specified from your system files)

Executed!!

### Required packages

The code 1 has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

- pytorch == 2.0.0

- numpy == 1.14.5

- sklearn == 0.24.2