Project Description:

The AI Movie Recommendation System is a web-based platform that uses Artificial Intelligence and Machine Learning algorithms to suggest movies to users based on their preferences, watch history, ratings, and behavior patterns. It delivers personalized recommendations much like platforms such as Netflix or Amazon Prime, enhancing the user’s experience by helping them discover content they’re likely to enjoy.

Core Idea:

This system leverages collaborative filtering, content-based filtering, or a hybrid approach to recommend movies. Users interact with the system through a simple, responsive interface, while the backend AI models analyze user data and movie features to generate personalized movie suggestions.

Key Features:

 1. User Registration & Login

• Secure user authentication.
• Each user has a profile with saved preferences and watch history.

 2. Movie Database

• Contains movies with metadata:
  • Genre
  • Director
  • Cast
  • Release year
  • IMDB rating
  • Description, poster, trailer links

 3. Recommendation Engine (AI/ML)

• Content-Based Filtering: Recommends movies similar to those the user has rated or liked.
• Collaborative Filtering: Suggests movies based on similar user preferences.
• Hybrid Model: Combines both for better accuracy.

 4. Search & Filter Options

• Search movies by title, genre, language, or rating.
• Filter by trending, most watched, recently added.

 5. Rating & Review System

• Users can rate movies from 1–5 stars.
• Add short reviews that help train the model.

 6. Watchlist Feature

• Save movies to “Watch Later”.
• Recently watched list maintained.

 7. Admin Panel

• Admin can add/remove/update movie details.
• Monitor user activity and feedback.

Tech Stack:

Frontend:

• HTML, CSS, Bootstrap – responsive UI
• JavaScript – dynamic elements and interactivity

Backend:

• PHP / Java / Node.js – server-side logic
• RESTful APIs to communicate with the frontend

Database:

• MySQL / MongoDB – store users, movies, ratings, and preferences

AI/ML Component:

• Python (with Flask/REST API) for recommendation engine
  • Libraries: scikit-learn, pandas, NumPy, Surprise, or TensorFlow
• The AI model can be integrated via API or run as a microservice

How It Works:

1. User logs in and watches/rates a few movies.
2. System collects data about likes/dislikes and behavior.
3. AI model processes this data to find patterns.
4. Based on preferences, recommends:
   • Similar movies (content-based)
   • Movies liked by similar users (collaborative)
5. The more a user interacts, the more accurate the recommendations.