What's New (May, 2024)

We have a new release Recommenders 1.2.0!

So many changes since our last release. We have full tests on Python 3.8 to 3.11 (around 1800 tests), upgraded performance in many algorithms, reviewed notebooks, and many more improvements.

Introduction

Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems.

Recommenders is a project under the Linux Foundation of AI and Data.

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 recommendation algorithm.
• Model: Building models using various classical and deep learning recommendation 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 recommendation models.
• Operationalize: Operationalizing models in a production environment on Azure.

Several utilities are provided in recommenders 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 Recommenders documentation.

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

For some of the practical scenarios where recommendation systems have been applied, see scenarios.

Getting Started

We recommend conda for environment management, and VS Code for development. To install the recommenders package and run an example notebook on Linux/WSL:

# 1. Install gcc if it is not installed already. On Ubuntu, this could done by using the command
# sudo apt install gcc

# 2. Create and activate a new conda environment
conda create -n <environment_name> python=3.9
conda activate <environment_name>

# 3. Install the core recommenders package. It can run all the CPU notebooks.
pip install recommenders

# 4. create a Jupyter kernel
python -m ipykernel install --user --name <environment_name> --display-name <kernel_name>

# 5. Clone this repo within VSCode or using command line:
git clone https://github.com/recommenders-team/recommenders.git

# 6. Within VSCode:
#   a. Open a notebook, e.g., examples/00_quick_start/sar_movielens.ipynb;  
#   b. Select Jupyter kernel <kernel_name>;
#   c. Run the notebook.

For more information about setup on other platforms (e.g., Windows and macOS) and different configurations (e.g., GPU, Spark and experimental features), see the Setup Guide.

In addition to the core package, several extras are also provided, including:

• [gpu]: Needed for running GPU models.
• [spark]: Needed for running Spark models.
• [dev]: Needed for development for the repo.
• [all]: [gpu]|[spark]|[dev]
• [experimental]: Models that are not thoroughly tested and/or may require additional steps in installation.

Algorithms

The table below lists the recommendation algorithms currently available in the repository. Notebooks are linked under the Example column as Quick start, showcasing an easy to run example of the algorithm, or as Deep dive, explaining in detail the math and implementation of the algorithm.

Algorithm	Type	Description	Example
Alternating Least Squares (ALS)	Collaborative Filtering	Matrix factorization algorithm for explicit or implicit feedback in large datasets, optimized for scalability and distributed computing capability. It works in the PySpark environment.	Quick start / Deep dive
Attentive Asynchronous Singular Value Decomposition (A2SVD)*	Collaborative Filtering	Sequential-based algorithm that aims to capture both long and short-term user preferences using attention mechanism. It works in the CPU/GPU environment.	Quick start
Cornac/Bayesian Personalized Ranking (BPR)	Collaborative Filtering	Matrix factorization algorithm for predicting item ranking with implicit feedback. It works in the CPU environment.	Deep dive
Cornac/Bilateral Variational Autoencoder (BiVAE)	Collaborative Filtering	Generative model for dyadic data (e.g., user-item interactions). It works in the CPU/GPU environment.	Deep dive
Convolutional Sequence Embedding Recommendation (Caser)	Collaborative Filtering	Algorithm based on convolutions that aim to capture both user’s general preferences and sequential patterns. It works in the CPU/GPU environment.	Quick start
Deep Knowledge-Aware Network (DKN)*	Content-Based Filtering	Deep learning algorithm incorporating a knowledge graph and article embeddings for providing news or article recommendations. It works in the CPU/GPU environment.	Quick start / Deep dive
Extreme Deep Factorization Machine (xDeepFM)*	Collaborative Filtering	Deep learning based algorithm for implicit and explicit feedback with user/item features. It works in the CPU/GPU environment.	Quick start
FastAI Embedding Dot Bias (FAST)	Collaborative Filtering	General purpose algorithm with embeddings and biases for users and items. It works in the CPU/GPU environment.	Quick start
LightFM/Factorization Machine	Collaborative Filtering	Factorization Machine algorithm for both implicit and explicit feedbacks. It works in the CPU environment.	Quick start
LightGBM/Gradient Boosting Tree*	Content-Based Filtering	Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems. It works in the CPU/GPU/PySpark environments.	Quick start in CPU / Deep dive in PySpark
LightGCN	Collaborative Filtering	Deep learning algorithm which simplifies the design of GCN for predicting implicit feedback. It works in the CPU/GPU environment.	Deep dive
GeoIMC*	Collaborative Filtering	Matrix completion algorithm that takes into account user and item features using Riemannian conjugate gradient optimization and follows a geometric approach. It works in the CPU environment.	Quick start
GRU	Collaborative Filtering	Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks. It works in the CPU/GPU environment.	Quick start
Multinomial VAE	Collaborative Filtering	Generative model for predicting user/item interactions. It works in the CPU/GPU environment.	Deep dive
Neural Recommendation with Long- and Short-term User Representations (LSTUR)*	Content-Based Filtering	Neural recommendation algorithm for recommending news articles with long- and short-term user interest modeling. It works in the CPU/GPU environment.	Quick start
Neural Recommendation with Attentive Multi-View Learning (NAML)*	Content-Based Filtering	Neural recommendation algorithm for recommending news articles with attentive multi-view learning. It works in the CPU/GPU environment.	Quick start
Neural Collaborative Filtering (NCF)	Collaborative Filtering	Deep learning algorithm with enhanced performance for user/item implicit feedback. It works in the CPU/GPU environment.	Quick start / Deep dive
Neural Recommendation with Personalized Attention (NPA)*	Content-Based Filtering	Neural recommendation algorithm for recommending news articles with personalized attention network. It works in the CPU/GPU environment.	Quick start
Neural Recommendation with Multi-Head Self-Attention (NRMS)*	Content-Based Filtering	Neural recommendation algorithm for recommending news articles with multi-head self-attention. It works in the CPU/GPU environment.	Quick start
Next Item Recommendation (NextItNet)	Collaborative Filtering	Algorithm based on dilated convolutions and residual network that aims to capture sequential patterns. It considers both user/item interactions and features. It works in the CPU/GPU environment.	Quick start
Restricted Boltzmann Machines (RBM)	Collaborative Filtering	Neural network based algorithm for learning the underlying probability distribution for explicit or implicit user/item feedback. It works in the CPU/GPU environment.	Quick start / Deep dive
Riemannian Low-rank Matrix Completion (RLRMC)*	Collaborative Filtering	Matrix factorization algorithm using Riemannian conjugate gradients optimization with small memory consumption to predict user/item interactions. It works in the CPU environment.	Quick start
Simple Algorithm for Recommendation (SAR)*	Collaborative Filtering	Similarity-based algorithm for implicit user/item feedback. It works in the CPU environment.	Quick start / Deep dive
Self-Attentive Sequential Recommendation (SASRec)	Collaborative Filtering	Transformer based algorithm for sequential recommendation. It works in the CPU/GPU environment.	Quick start
Short-term and Long-term Preference Integrated Recommender (SLi-Rec)*	Collaborative Filtering	Sequential-based algorithm that aims to capture both long and short-term user preferences using attention mechanism, a time-aware controller and a content-aware controller. It works in the CPU/GPU environment.	Quick start
Multi-Interest-Aware Sequential User Modeling (SUM)*	Collaborative Filtering	An enhanced memory network-based sequential user model which aims to capture users' multiple interests. It works in the CPU/GPU environment.	Quick start
Sequential Recommendation Via Personalized Transformer (SSEPT)	Collaborative Filtering	Transformer based algorithm for sequential recommendation with User embedding. It works in the CPU/GPU environment.	Quick start
Standard VAE	Collaborative Filtering	Generative Model for predicting user/item interactions. It works in the CPU/GPU environment.	Deep dive
Surprise/Singular Value Decomposition (SVD)	Collaborative Filtering	Matrix factorization algorithm for predicting explicit rating feedback in small datasets. It works in the CPU/GPU environment.	Deep dive
Term Frequency - Inverse Document Frequency (TF-IDF)	Content-Based Filtering	Simple similarity-based algorithm for content-based recommendations with text datasets. It works in the CPU environment.	Quick start
Vowpal Wabbit (VW)*	Content-Based Filtering	Fast online learning algorithms, great for scenarios where user features / context are constantly changing. It uses the CPU for online learning.	Deep dive
Wide and Deep	Collaborative Filtering	Deep learning algorithm that can memorize feature interactions and generalize user features. It works in the CPU/GPU environment.	Quick start
xLearn/Factorization Machine (FM) & Field-Aware FM (FFM)	Collaborative Filtering	Quick and memory efficient algorithm to predict labels with user/item features. It works in the CPU/GPU environment.	Deep dive

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.

Algorithm	Type	Description	Example
SARplus *	Collaborative Filtering	Optimized implementation of SAR for Spark	Quick start

Algorithm 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
BiVAE	0.146126	0.475077	0.411771	0.219145	N/A	N/A	N/A	N/A
BPR	0.132478	0.441997	0.388229	0.212522	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
LightGCN	0.088526	0.419846	0.379626	0.144336	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
SAR	0.110591	0.382461	0.330753	0.176385	1.253805	1.048484	-0.569363	0.030474
SVD	0.012873	0.095930	0.091198	0.032783	0.938681	0.742690	0.291967	0.291971

Contributing

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

This project adheres to this Code of Conduct in order to foster a welcoming and inspiring community for all.

Build Status

These tests are the nightly builds, which compute the asynchronous tests. main is our principal branch and staging is our development branch. We use pytest for testing python utilities in recommenders and the Recommenders notebook executor for the notebooks.

For more information about the testing pipelines, please see the test documentation.

AzureML Nightly Build Status

The nightly build tests are run daily on AzureML.

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

References

• FREE COURSE: M. González-Fierro, "Recommendation Systems: A Practical Introduction", LinkedIn Learning, 2024. Available on this link.
• D. Li, J. Lian, L. Zhang, K. Ren, D. Lu, T. Wu, X. Xie, "Recommender Systems: Frontiers and Practices", Springer, Beijing, 2024. Available on this link.
• 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
• 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
• 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.