Recommender Engines Project

Small project to explore hybrid recommender systems as part of my Recommender Engines course.

Overview

This project is a Flask-based web application that implements a hybrid recommender system. The system generates personalized product recommendations using both collaborative filtering and content based filtering. Users can receive recommendations by either selecting items or providing a user ID. There is a (very simple and not pretty) UI to be able to interact with the recommendations. The data was freely available online (anonymized) and linked in the data folder.

Features

• Homepage: Displays a list of items that users can select for generating recommendations.
• Recommendation System:
  • Recommends items based on a given user ID. OR
  • Recommends items based on selected items from the item list.
• Interactive API: RESTful API endpoint to generate recommendations dynamically.
• Method: Both collaborative (based on similar users) and content (based on similar content) recommender methods.

Running the project:

After installing the requirements, run: python src/app.py

Below is an example of what the UI looks like: First select items you are interested in:

Then you will get recommendations for items that you might like: