streamsight 2.0.2

A toolkit for offline evaluation of Recommender Systems.

Streamsight

Streamsight is an offline Reccomender Systems (RecSys) evaluation toolkit that respects a global timeline. The aim is to partition the data into different windows where data is incrementally released for the programmer to fit, train and submit predictions. This aims to provide a close simulation of an online setting when evaluating RecSys algorithms.

Full Flow Structure

     

Getting Started

Clone the repository

git clone https://github.com/hiiamtzekean/streamsight
cd streamsight

Dependencies can be installed with uv for ease of management.

uv sync

Alternatively, you may install dependencies locally with pip and venv

python3 -m venv venv
source venv/bin/activate
pip install -e .

The dependencies are listed in pyproject.toml.

Contributing

• We welcome all contributors, be it reporting an issue, or raising a pull request to fix an issue.
• When you make changes, rerun pip install . to test your changes.

Documentation

The documentation can be found here and repository on Github.

Citation

If you use this library in any part of your work, please cite the following papers:

Ng, T. K. (2024). Streamsight: a toolkit for offline evaluation of recommender systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181114