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Recommenders

An open-source Python library and collection of notebooks for developing recommendation systems, including rating, ranking, evaluation and operationalization workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Recommenders — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a project requires Python-based experimentation, evaluation or development of recommendation systems and benefits from notebook examples and best-practice implementations.

Who it is for

  • Python developers building recommendation features
  • Data scientists experimenting with rating or ranking models
  • Machine-learning teams evaluating recommendation approaches
  • Teams developing recommendation-system training or operationalization workflows

Documented uses

  • Prototype personalized product or content recommendations
  • Experiment with rating-prediction workflows
  • Evaluate ranking models in notebooks
  • Develop recommendation-system training pipelines
  • Explore recommendation-system operationalization
Before you choose

Requirements & limits

The repository is public and identified as open source under the MIT license. Technical metadata lists Linux, Windows, macOS, cloud and self-hosted platforms. No account or pricing details are established here.

  • Python is the primary language.
  • The resource combines a library with notebooks and best-practice implementations.
  • Its scope includes recommendation, rating, ranking, machine learning and operationalization.
  • The repository is public, active and not archived.
  • The MIT license may matter when assessing reuse and integration options.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Recommenders and run the SAR MovieLens notebook on Linux or WSL

Create a Python 3.11 environment, install the core package and Jupyter kernel, clone the repository, and run the documented SAR MovieLens example.

Before you start

  • Linux or WSL
  • gcc installed
  • uv installed or installable
  • Python 3.11
  • VS Code with Jupyter support

Steps

  1. Install gcc if it is not already installed, then install uv.

  2. Create and activate a virtual environment with the command below.

    Documented command
    uv venv ~/.venvs/recommenders --python 3.11

  3. Install the core package and Jupyter kernel with the command below and the command below, then register the recommenders kernel.

    Documented command
    uv pip install recommenders
    Documented command
    uv pip install ipykernel

  4. Clone the repository, open examples/00_quick_start/sar_movielens.ipynb in VS Code, select Python (recommenders), and run the notebook.

Expected outcome

The documented SAR MovieLens quick-start notebook is opened and run with the Recommenders environment.

Guide limitations

  • This procedure is documented for Linux/WSL; other platforms and GPU, Spark, or experimental configurations require the Setup Guide.
  • The core package is documented as supporting CPU notebooks; GPU and Spark models require their respective extras.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
recommenders-team/recommenders · Official homepage title · retained in its source language
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