disigma/recommenders

Best Practices on Recommendation Systems

Recommenders

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 at the wiki page.

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 to create a conda environment: (This is for a basic python environment, see SETUP.md for PySpark and GPU environment setup)

   cd Recommenders
   python scripts/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

   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)".

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.

Install this repository via PIP

A setup.py file is provided in order to simplify the installation of this utilities in this repo from the main directory. This still requires the conda environment to be installed as described above. Once the necessary dependencies are installed you can use the following command to install reco_utils as it's own python package.

pip install -e reco_utils

It is also possible to install directly from Github. Or from a specific branch as well.

pip install -e git+https://github.com/microsoft/recommenders/#egg=pkg\&subdirectory=reco_utils
pip install -e git+https://github.com/microsoft/recommenders/@staging#egg=pkg\&subdirectory=reco_utils

NOTE - The pip installation does not install any of the necessary package dependencies, it is expected that conda will be used as shown above to setup the environment for the utilities being used.

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.

Algorithm	Environment	Type	Description
Alternating Least Squares (ALS)	PySpark	Collaborative Filtering	Matrix factorization algorithm for explicit or implicit feedback in large datasets, optimized by Spark MLLib for scalability and distributed computing capability
Deep Knowledge-Aware Network (DKN)*	Python CPU / Python GPU	Content-Based Filtering	Deep learning algorithm incorporating a knowledge graph and article embeddings to provide powerful news or article recommendations
Extreme Deep Factorization Machine (xDeepFM)*	Python CPU / Python GPU	Hybrid	Deep learning based algorithm for implicit and explicit feedback with user/item features
FastAI Embedding Dot Bias (FAST)	Python CPU / Python GPU	Collaborative Filtering	General purpose algorithm with embeddings and biases for users and items
LightGBM/Gradient Boosting Tree*	Python CPU / PySpark	Content-Based Filtering	Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems
Neural Collaborative Filtering (NCF)	Python CPU / Python GPU	Collaborative Filtering	Deep learning algorithm with enhanced performance for implicit feedback
Restricted Boltzmann Machines (RBM)	Python CPU / Python GPU	Collaborative Filtering	Neural network based algorithm for learning the underlying probability distribution for explicit or implicit feedback
Riemannian Low-rank Matrix Completion (RLRMC)*	Python CPU	Collaborative Filtering	Matrix factorization algorithm using Riemannian conjugate gradients optimization with small memory consumption.
Simple Algorithm for Recommendation (SAR)*	Python CPU	Collaborative Filtering	Similarity-based algorithm for implicit feedback dataset
Surprise/Singular Value Decomposition (SVD)	Python CPU	Collaborative Filtering	Matrix factorization algorithm for predicting explicit rating feedback in datasets that are not very large
Vowpal Wabbit Family (VW)*	Python CPU (online training)	Content-Based Filtering	Fast online learning algorithms, great for scenarios where user features / context are constantly changing
Wide and Deep	Python CPU / Python GPU	Hybrid	Deep learning algorithm that can memorize feature interactions and generalize user features

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

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.

Algo	MAP	nDCG@k	Precision@k	Recall@k	RMSE	MAE	R2	Explained Variance
ALS	0.004732	0.044239	0.048462	0.017796	0.965038	0.753001	0.255647	0.251648
SVD	0.012873	0.095930	0.091198	0.032783	0.938681	0.742690	0.291967	0.291971
SAR	0.113028	0.388321	0.333828	0.183179	N/A	N/A	N/A	N/A
NCF	0.107720	0.396118	0.347296	0.180775	N/A	N/A	N/A	N/A
FastAI	0.025503	0.147866	0.130329	0.053824	0.943084	0.744337	0.285308	0.287671

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
Windows CPU	master			staging
Windows GPU	master			staging
Windows Spark	master			staging

AzureML Build Status

These DevOps pipelines run the existing tests on AzureML.

Build Type	Branch	Status		Branch	Status
nightly_cpu_tests	master			Staging
nightly_gpu_tests	master			Staging

NOTE - these tests are the nightly builds, which compute the smoke and integration tests. Master is our main 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.