Streamlit-based Recommender System

Description of contents

Below is a high-level description of the contents within this repo:

File Name	Description
edsa_recommender.py	Base Streamlit application definition.
recommenders/collaborative_based.py	Simple implementation of collaborative filtering.
recommenders/content_based.py	Simple implementation of content-based filtering.
resources/data/	Sample movie and rating data used to demonstrate app functioning. NOTE: The dataset includes mostly movies from the 90s.
resources/models/	Folder to store model and data binaries if produced.
utils/	Folder to store additional helper functions for the Streamlit app.

Usage Instructions

Running the recommender system locally

As a first step to becoming familiar with our web app's functioning, we recommend setting up a running instance on your own local machine.

To do this, follow the steps below by running the given commands within a Git bash (Windows), or terminal (Mac/Linux):

1. Ensure that you have the prerequisite Python libraries installed on your local machine:

pip install -U streamlit numpy pandas scikit-learn
conda install -c conda-forge scikit-surprise

2. Clone the forked repo to your local machine.

git clone https://github.com/{your-account-name}/unsupervised-predict-streamlit-template.git

3. Navigate to the base of the cloned repo, and start the Streamlit app.

cd unsupervised-predict-streamlit-template/
streamlit run edsa_recommender.py

If the web server was able to initialise successfully, the following message should be displayed within your bash/terminal session:

  You can now view your Streamlit app in your browser.

    Local URL: http://localhost:8501
    Network URL: http://192.168.43.41:8501

You should also be automatically directed to the base page of your web app. The page should look like this:

Congratulations! You've now officially deployed your web-based recommender engine!