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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.
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.
This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:
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.
We recommend conda for environment management, and VS Code for development. To install the recommenders package and run an example notebook on Linux/WSL:
