Recommenders

What's New (February 4, 2021)

We have a new relase Recommenders 2021.2!

It comes with lots of bug fixes, optimizations and 3 new algorithms, GeoIMC, Standard VAE and Multinomial VAE. We also added tools to facilitate the use of Microsoft News dataset (MIND). In addition, we publised our KDD2020 tutorial where we built a recommender of COVID papers using Microsoft Academic Graph.

We also changed the default branch from master to main. Now when you download the repo, you will get main branch.

See past announcements in NEWS.md.

Introduction

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

• Prepare Data: Preparing and loading data for each recommender algorithm
• Model: Building models using various classical and deep learning recommender algorithms such as Alternating Least Squares (ALS) or eXtreme Deep Factorization Machines (xDeepFM).
• Evaluate: Evaluating algorithms with offline metrics
• Model Select and Optimize: Tuning and optimizing hyperparameters for recommender models
• 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 training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the reco_utils documentation.

For a more detailed overview of the repository, please see the documents on the wiki page.

Getting Started

Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) 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 to create a conda environment: (This is for a basic python environment, see SETUP.md for PySpark and GPU environment setup)

cd Recommenders
python tools/generate_conda_file.py
conda env create -f reco_base.yaml  

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

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

5. Start the Jupyter notebook server

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)".

NOTE - The Alternating Least Squares (ALS) notebooks require a PySpark environment to run. Please follow the steps in the setup guide to run these notebooks in a PySpark environment. For the deep learning algorithms, it is recommended to use a GPU machine.

Algorithms

The table below lists the recommender algorithms currently available in the repository. Notebooks are linked under the Environment column when different implementations are available.

NOTE: ^* indicates algorithms invented/contributed by Microsoft.

Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests.

Preliminary Comparison

We provide a benchmark notebook to illustrate how different algorithms could be evaluated and compared. In this notebook, the MovieLens dataset is split into training/test sets at a 75/25 ratio using a stratified split. 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 recommended items). We run the comparison on a Standard NC6s_v2 Azure DSVM (6 vCPUs, 112 GB memory and 1 P100 GPU). Spark ALS is run in local standalone mode. In this table we show the results on Movielens 100k, running the algorithms for 15 epochs.

Contributing

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

Build Status

These tests are the nightly builds, which compute the smoke and integration tests. main is our principal branch and staging is our development branch. We use pytest for testing python utilities in reco_utils and papermill for the notebooks. For more information about the testing pipelines, please see the test documentation.

DSVM Build Status

The following tests run on a Windows and Linux DSVM daily. These machines run 24/7.

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

Related projects

• Microsoft AI Github: Find other Best Practice projects, and Azure AI design patterns in our central repository.
• NLP best practices: Best practices and examples on NLP.
• Computer vision best practices: Best practices and examples on computer vision.
• Forecasting best practices: Best practices and examples on time series forecasting.

Reference papers

• A. Argyriou, M. González-Fierro, and L. Zhang, "Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems", WWW 2020: International World Wide Web Conference Taipei, 2020. Available online: https://dl.acm.org/doi/abs/10.1145/3366424.3382692
• L. Zhang, T. Wu, X. Xie, A. Argyriou, M. González-Fierro and J. Lian, "Building Production-Ready Recommendation System at Scale", ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2019 (KDD 2019), 2019.
• S. Graham, J.K. Min, T. Wu, "Microsoft recommenders: tools to accelerate developing recommender systems", RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems, 2019. Available online: https://dl.acm.org/doi/10.1145/3298689.3346967