recommenders-team/recommenders

What it solves

Recommenders helps researchers and developers prototype, experiment with, and deploy recommendation systems. It provides a comprehensive collection of classic and state-of-the-art algorithms, reducing the need to build these complex models from scratch.

How it works

The project is organized around five key tasks for building a recommendation pipeline:

1. Prepare Data: Tools and examples for loading and formatting data for specific algorithms.
2. Model: Implementations of various collaborative filtering and content-based filtering algorithms (e.g., ALS, xDeepFM, LightGCN).
3. Evaluate: Tools for measuring model performance using offline metrics.
4. Model Select and Optimize: Methods for tuning hyperparameters to improve accuracy.
5. Operationalize: Guidance and examples for deploying models into production environments.

Who it’s for

It is designed for AI researchers, software developers, and enthusiasts who want to build recommendation systems using Python, PySpark, or GPU-accelerated environments.

Highlights

• Extensive Algorithm Library: Includes a wide range of models, from matrix factorization (ALS, SVD) to deep learning and Transformer-based sequential recommenders (SASRec, SSEPT).
• Jupyter Notebook Examples: Provides "Quick start" and "Deep dive" notebooks for each algorithm to facilitate learning and implementation.
• Flexible Compute: Supports CPU, GPU, and PySpark environments depending on the algorithm.
• Benchmarking: Includes benchmark notebooks to compare the performance of different algorithms on standard datasets like MovieLens.