Movie4AllMoods

About the Project

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.

Key Features

• •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