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A movie recommendation website that suggests films based on a user's ratings and on similar users, combining collaborative filtering with content-based similarity.
Problem Statement
Streaming catalogues are too large to browse. Recommendation engines are central to modern products, and students need a clear project to learn how they work.
Objectives
Implement user-based and item-based collaborative filtering
Add content-based similarity using genres and cast
Handle the cold start problem for new users
Evaluate recommendations with RMSE and precision at K
Modules
Data Preparation
Loads and cleans the MovieLens dataset and builds the ratings matrix.
Recommendation Engine
Collaborative and content-based models with a hybrid ranking step.
User Accounts
Sign up, rate movies, and maintain a watchlist.
Web Interface
Search, movie detail pages, and a personalised recommendations feed.
Expected Output
A website where users rate a few movies and immediately get a personalised list of recommended films with similarity explanations.
Future Scope
Matrix factorization and neural recommenders, real-time updates, and poster and trailer integration through a movie API.