Recommenders

This repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on four key tasks:

1. Prepare Data: Preparing and loading data for each recommender algorithm
2. Model: Building models using various recommender algorithms such as Alternating Least Squares (ALS), Singular Value Decomposition (SVD), etc.
3. Evaluate: Evaluating algorithms with offline metrics
4. Operationalize: Operationalizing models in a production environment on Azure

Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications.

Getting Started

Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.

To setup on your local machine:

1. Install Anaconda with Python >= 3.6. Miniconda is a quick way to get started.
2. Clone the repository

   git clone https://github.com/Microsoft/Recommenders
   

3. Run the generate conda file script and create a conda environment:

   cd Recommenders
   ./scripts/generate_conda_file.sh
   conda env create -n reco -f conda_bare.yaml  
   

4. Activate the conda environment and register it with Jupyter:

   conda activate reco
   python -m ipykernel install --user --name reco --display-name "Python (reco)"
   

5. Start the Jupyter notebook server

   cd notebooks
   jupyter notebook
   

6. Run the SAR Python CPU Movielens notebook under the 00_quick_start folder. Make sure to change the kernel to "Python (reco)".

Notebooks

We provide several notebooks to show how recommendation algorithms can be designed, evaluated and operationalized.

• The Quick-Start Notebooks detail how you can quickly get up and run with state-of-the-art algorithms such as the Smart Adaptive Recommendation (SAR) algorithm and ALS algorithm.

• The Data Preparation Notebook shows how to prepare and split data properly for recommendation systems.

• The Modeling Notebooks provide a deep dive into implementations of different recommender algorithms.

• The Evaluation Notebooks show how to evaluate recommender algorithms for different ranking and rating metrics.

• The Operationalizion Notebook demonstrates how to deploy models in production systems.

In addition, we also provide a comparison notebook to illustrate how different algorithms could be evaluated and compared. In this notebook, data (MovieLens 1M) is randomly split into train/test sets at a 75/25 ratio. A recommendation model is trained using each of the collaborative filtering algorithms below. We utilize empirical parameter values reported in literature here. For ranking metrics we use k = 10 (top 10 results). We run the comparison on a Standard NC6s_v2 Azure DSVM (6 vCPUs, 112 GB memory and 1 K80 GPU). Spark ALS is run in local standalone mode.

Preliminary Comparison

Algo	MAP	nDCG@k	Precision@k	Recall@k	RMSE	MAE	R2	Explained Variance
ALS	0.002020	0.024313	0.030677	0.009649	0.860502	0.680608	0.406014	0.411603
SAR	0.064013	0.308012	0.277215	0.109292	N/A	N/A	N/A	N/A
SVD	0.010915	0.102398	0.092996	0.025362	0.888991	0.696781	0.364178	0.364178

Contributing

This project welcomes contributions and suggestions. Before contributing, please see our contribution guidelines.

Build Status

Build Type	Branch	Status		Branch	Status
Linux CPU	master			staging
Linux GPU	master			staging
Linux Spark	master			staging

NOTE - the tests are executed every night, we use pytest for testing python utilities in reco_utils and notebooks.