Introduction to Recommenders Project
The Recommenders project is developed to aid researchers, developers, and enthusiasts in creating, testing, and deploying a variety of recommendation systems. This project provides valuable resources for both classic and cutting-edge recommendation strategies.
Project Overview
Recommenders is a proud project under the Linux Foundation of AI and Data. This repository primarily consists of examples and best practices for building recommendation systems, all provided as Jupyter notebooks. The examples cover five crucial areas:
- Prepare Data: Instructions for preparing and loading data to align with various recommendation algorithms.
- Model Building: Guidelines for constructing models using a range of classical and advanced algorithms, such as Alternating Least Squares (ALS) and eXtreme Deep Factorization Machines (xDeepFM).
- Evaluate: Techniques for assessing algorithms utilizing offline metrics.
- Model Selection and Optimization: Tips for fine-tuning and optimizing recommendation models' hyperparameters.
- Operationalize: Methods for implementing models in a production environment, primarily on Azure.
The project includes a suite of utilities designed to assist with common tasks such as loading datasets, evaluating model outputs, and segregating training and test data. To further aid learning and customization, several state-of-the-art algorithms are available for integration into bespoke applications.
Getting Started
The Recommenders project emphasizes using conda for environment management and VS Code for development. Below are the steps to get started on Linux/WSL:
-
Install gcc: If not installed, use the command
sudo apt install gcc. -
Create and Activate a Conda Environment:
conda create -n <environment_name> python=3.9 conda activate <environment_name> -
Install Recommenders Package:
pip install recommenders -
Create a Jupyter Kernel:
python -m ipykernel install --user --name <environment_name> --display-name <kernel_name> -
Clone the Repository:
git clone https://github.com/recommenders-team/recommenders.git -
Run Example Notebooks in VS Code by opening a notebook and selecting the appropriate Jupyter kernel.
For additional setups on different platforms (Windows, macOS) and configurations (GPU, Spark), refer to the setup guidelines.
Supported Algorithms
The Recommenders project offers a wide range of algorithms, each aimed to cater to specific recommendation needs. Some of the available algorithms include:
- ALS: Suitable for collaborative filtering and optimized for scalability.
- BPR: Ideal for predicting item ranking based on implicit feedback.
- xDeepFM: A deep learning-based algorithm effective for feedback with user and item features.
- LightGBM: Fast and efficient for content-based filtering tasks.
- SAR: A simple algorithm for similarity-based recommendations.
Each algorithm is backed by examples, showcasing quick-start guides or in-depth explanations.
Contributor and Community Engagement
Recommenders welcome contributions from the community. It encourages potential contributors to familiarize themselves with the guidelines before engaging. This project follows Microsoft's Open Source Code of Conduct to maintain a welcoming environment.
Conclusion
In summary, the Recommenders project is a comprehensive resource for developing sophisticated recommendation systems. It offers tools, examples, and algorithms essential for anyone wanting to explore this fascinating domain. Whether one is starting with data preparation or seeking to operationalize models, Recommenders cover it all.