Lightweight content-based movie recommender that converts movie metadata (titles, genres, descriptions) into TF‑IDF vectors and ranks films by cosine similarity. Clean, modular Python code with example notebooks and minimal dependencies—easy to extend and integrate.

A content based movie recommender system using Machine Learning and Flask.

A content-based recommender system using TF-IDF and cosine similarity to suggest personalized items based on user profiles.

A movie manager web app, with CLI support

By 2020, Board Game Geek featured over 36,000 board games, with 5,000 new games introduced that year alone. To manage this rapid influx and effectively connect players with the right games, the team developed a Meta-level hybrid recommendation system for the Board Game Geek platform.

A content-based recommender system designed to help Communication Science Master's students at the UvA find suitable thesis supervisors based on their research interests.

This project delivers a seamless recommendation experience by blending machine learning similarity models with The Movie Database (TMDB) API for real-time posters, summaries, and release details. Lightweight, fast, and designed for production-grade deployment.

Recommend movies based on user preferences