Project Name: Movie Recommendation System

Description:

The Movie Recommendation System project aims to develop a web application that provides personalized movie recommendations to users based on their preferences and viewing history. Leveraging machine learning algorithms and collaborative filtering techniques, the system analyzes user behavior and movie features to suggest relevant and engaging movie options. The goal is to enhance the user's movie-watching experience by offering tailored recommendations that align with their tastes and interests.

Features:

Personalized Recommendations: Offer movie suggestions tailored to each user's preferences and past viewing habits.

User Profiles: Allow users to create profiles and customize their preferences, including favorite genres, actors, directors, and more.

Movie Catalog: Maintain a comprehensive catalog of movies, including metadata such as genres, release year, ratings, and user reviews.

Rating and Feedback: Enable users to rate movies and provide feedback, which helps improve the accuracy of future recommendations.

Recommendation Algorithms: Utilize collaborative filtering, content-based filtering, and hybrid recommendation techniques to generate diverse and relevant movie recommendations.

Integration with Streaming Platforms: Seamlessly integrate with streaming platforms to enable users to watch recommended movies directly from the application.

Search and Filtering: Allow users to search for movies by title, genre, actor, or director, and apply filters to narrow down their options.

Social Sharing: Enable users to share their favorite movies and recommendations with friends and social networks.