streamsight

Streamsight

The purpose of this Final Year Project is to design and implement a toolkit for evaluating Recommendation System (RecSys) which respects the temporal aspect during the data splitting process and incrementally release data as close to a live production setting as possible. We aim to achieve this through provision of API for the programmer to interact with the objects in the library.

     

Table of Contents

• Streamsight
  • Table of Contents
  • Installation with Github
    • Installation through poetry
    • Installation through pip
  • Installation with PyPI
  • Documentation
  • Report and Citation

Installation with Github

The package can be installed quickly with python poetry or the traditional pip method. The recommended way of installation would be through poetry as it will help install the dependencies along with the package. We assume that the repository has already been cloned else you can run the following code on terminal before continuing.

git clone https://github.com/HiIAmTzeKean/Streamsight.git
cd Streamsight

Installation through poetry

The following code assumes that you do not have poetry installed yet. If you using MacOS, you might want to consider installing poetry with homebrew instead.

pip install poetry
# MacOS can consider using brew install poetry
poetry install

Installation through pip

The following code below assumes that you have pip installed and is in system PATH.

pip install -e .

Installation with PyPI

Alternatively streamsight is available on PyPi and can be installed through either of the commands below. The link to PyPI can be found here.

# To install via pip
pip install streamsight

# To install with streamsight as a dependency
poetry add streamsight

Documentation

The documentation can be found here and repository on Github.

Report and Citation

The report for this project Streamsight: a toolkit for offline evaluation of recommender systems can be found in the NTU repository.

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