Emotion based Multimodal Music Recommendation System

Multi-modal Knowledge Graph Convolutional Networks for Music Recommender 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.

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)

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

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
Angry.png		Angry.png
Configure_Data.ipynb		Configure_Data.ipynb
Convolutional Layer.png		Convolutional Layer.png
Details.png		Details.png
EMKGCN Data Loader.ipynb		EMKGCN Data Loader.ipynb
EMKGCN Main.ipynb		EMKGCN Main.ipynb
Happy (2).png		Happy (2).png
Happy.png		Happy.png
Multimodal_aggregator.ipynb		Multimodal_aggregator.ipynb
No Face Detected.png		No Face Detected.png
PyQt5.ipynb		PyQt5.ipynb
README.md		README.md
Spotipy.ipynb		Spotipy.ipynb
Suprised.png		Suprised.png
Training_Spotipy.ipynb		Training_Spotipy.ipynb
Utils.ipynb		Utils.ipynb
angry.csv		angry.csv
camera.cpython-38.pyc		camera.cpython-38.pyc
data_loader.cpython-36.pyc		data_loader.cpython-36.pyc
disgusted.csv		disgusted.csv
fearful.csv		fearful.csv
happy.csv		happy.csv
itemID2entityID.txt		itemID2entityID.txt
kg.txt		kg.txt
model.cpython-36.pyc		model.cpython-36.pyc
model.h5		model.h5
multimodal_aggregator.cpython-36.pyc		multimodal_aggregator.cpython-36.pyc
neighbor_aggregator.cpython-36.pyc		neighbor_aggregator.cpython-36.pyc
neutral.csv		neutral.csv
sad.csv		sad.csv
surprised.csv		surprised.csv
train.cpython-36.pyc		train.cpython-36.pyc
userID2itemID4ratings.tsv		userID2itemID4ratings.tsv
utils.cpython-36.pyc		utils.cpython-36.pyc