09Machine Learning
Movie4AllMoods
A web application that curates movies based on user moods and preferences.
Project preview01 / 01
The idea behind it
A sophisticated movie recommendation platform that helps you discover films based on your mood, preferences, and viewing history. Built with Django and modern web technologies, Movie4AllMoods combines content-based filtering with TF-IDF vectorization and cosine similarity algorithms to deliver personalized movie recommendations. Features include mood-based discovery across 9 emotional states, intelligent watchlist management, and a hybrid recommendation system with serendipity elements to help users discover hidden gems.
What it does
- Mood-based recommendations across 9 emotional states (happy, sad, excited, romantic, etc.)
- Content-based filtering using cast, crew, genre, and metadata
- Enhanced similarity algorithm with IMDb scores, release decades, and runtime consideration
- Personalized recommendations with time decay and popularity boosting
- Smart watchlist management with Plan to Watch, Watching, and Completed categories
- Rating system (1-10 scale) that improves future recommendations
- Serendipity recommendations to discover hidden gems outside comfort zone
- Advanced filtering with 17+ genres, runtime, and release year options
- Cached similarity matrices for sub-100ms recommendation response times
Challenges & solutions
- Implementing efficient cosine similarity computation for large movie datasets
- Balancing personalized recommendations with serendipity for content discovery
- Optimizing database queries with proper indexing for fast response times
- Managing genre diversity to prevent recommendation echo chambers
- Implementing time decay for ratings to prioritize recent user preferences
- Creating an intuitive mood-to-genre mapping system
Outcomes & impact
- Hybrid recommendation system combining content-based filtering with collaborative elements
- Sub-100ms query response times through database indexing and caching
- Successfully processes and recommends from a comprehensive movie database
- Achieved genre diversity control with maximum 40% from single genre
- Implemented serendipity feature with 10-15% outlier recommendations for discovery