Open Music Recommendation System (Open MRS) by BerryAI
Acai is an open source project initialised by Berry Labs, a startup working towards machine learning algorithms to solve daily issues. Acai (codename) is trying to solve the problem of The Tyranny of Choice (a.k.a Paradox of Choice) to describe the misery of users facing over-abundant choices. In the music area, especially in the age of streaming music, this paradox becomes so significant that it affects every single piece of choice when users try to enjoy music. It's why this project was born. http://www.acai.berry.ai/
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
What things you need to install the software and how to install them
Python Python-Librosa Python-Lutorpy Python-NumPy Python-Scipy Torch Torch-cunn Torch-dp Torch-nn Torch-optim Torch-xlua
All major distributions of Linux provide packages for both Python and NumPy.
Mac OS X
pip install librosa pip install lutorpy pip install numpy pip install scipy
Personally, I will recommend Anaconda as default Python compiler. To install them, go to page
and find the proper install packages
You can find torch installation instruction in the official site: http://torch.ch/ You can also refer to our wiki page: https://github.com/BerryAI/music_cortex/wiki/Torch-Setup
In this project, we use some public open database, and they are
- Last.fm 1k user data download
- Million Song Databasedownload
- Million Song Database subset download
- Echo Nest user data download
For convenience purpose, I have calculate the intersection between 1k user data and MSD Database. HERE is the download link.
Test functions are under ./test folder. After downloading all the data files, please put the extracted files into ./data folder.
Then run collaborative filtering
run convolutional neural networks
in command line under the directory of the project installed.
Collaborative Filtering Methods
* Memory based recommendation
* Matrix Factorization and Hidden Features
Normally, we have two different approaches:
- Singular Value Decomposition
- M is m*m unitary matrix
- Σ is m*n diagonal matrix with singular values
- N is n*n unitary matrix
we have two different approaches:
Both methods will converge, but please be careful choosing coefficients.
Convolutional Neural Networks
* Building Blocks
Convolutional layers are the core building block of CNNs. The layer's parameters consist of sets of learnable filters/kernels . During training, the parameters are learned from data in order to solve the target problem. The forward equation is:
where is the data in layer L in filter i, and * represents convolution operation.
2.Max Pooling Layer
Pooling layer is another important concept of CNN. It is down-sampling process. Max pooling is a non-linear down-sampling method. The forward equation is:
where p,q are the pooling size.
3.Rectified Linear Units Layer
ReLU layers apply nonlinear activation function to neurons. Comparing to other common activation functions, ReLU is fast in training and suffers less on gradient extenuation during training.The forward equation is:
4.Loss Layer The loss layer is the last layer in CNN which defines the training deviation between real predicted results and target results. We provide 2 options in our model.
* Back-propagation Rule
- Fork it!
- Create your feature branch:
git checkout -b my-new-feature
- Commit your changes:
git commit -am 'Add some feature'
- Push to the branch:
git push origin my-new-feature
- Submit a pull request :D
The OpenMRS source code and binaries are released under the MIT license