Contents

recommenders 1.2.1

0

Recommenders - Python utilities for building recommendation systems

Recommenders - Python utilities for building recommendation systems

Stars: 21449, Watchers: 21449, Forks: 3292, Open Issues: 170

The recommenders-team/recommenders repo was created 7 years ago and the last code push was 3 days ago.
The project is extremely popular with a mindblowing 21449 github stars!

How to Install recommenders

You can install recommenders using pip

pip install recommenders

or add it to a project with poetry

poetry add recommenders

Package Details

Author
Recommenders contributors
License
None
Homepage
https://github.com/recommenders-team/recommenders
PyPi:
https://pypi.org/project/recommenders/
Documentation:
https://recommenders-team.github.io/recommenders/intro.html
GitHub Repo:
https://github.com/microsoft/recommenders

Classifiers

  • Scientific/Engineering/Artificial Intelligence
  • Software Development/Libraries/Python Modules
No  recommenders  pypi packages just yet.

Errors

A list of common recommenders errors.

Code Examples

Here are some recommenders code examples and snippets.

GitHub Issues

The recommenders package has 170 open issues on GitHub

  • [BUG] The link to the Contributing Guide is wrong
  • SASRec and SSEPT from TF to PyTorch
  • Replace conda by uv
  • Migrate testing workflows from AzureML to GitHub runners

See more issues on GitHub

Related Packages & Articles

pytorch-lightning 2.6.1

PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.

spacy 3.8.11

Industrial-strength Natural Language Processing (NLP) in Python

auto_ml 2.9.10

Automated machine learning for production and analytics

flwr 1.26.1

Flower: A Friendly Federated AI Framework

deeplake 4.5.2

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications. It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more.

fiftyone 1.13.0

FiftyOne: the open-source tool for building high-quality datasets and computer vision models

rexmex 0.1.3

A General Purpose Recommender Metrics Library for Fair Evaluation.