🎬🎵 Simple Collaborative Filtering Recommendation System

A Python-based recommendation system that suggests movies 🎥 and music 🎶 to users based on their shared preferences with other users.

🚀 Features

✅ User-Based Collaborative Filtering – Finds similar users based on shared ratings 👫
✅ Personalized Recommendations – Suggests movies & music users haven't watched/listened to yet 🎧🎞️
✅ Dynamic & Expandable – Easily add more users and ratings for better recommendations 📈
✅ Simple & Lightweight – No external libraries, just pure Python 🐍

📌 How It Works

1. User Ratings Dataset 🎭
   • Each user rates movies and songs on a scale of 1-5.
2. Finding Similar Users 🤝
   • Compares a target user’s ratings with others to find similar tastes.
3. Making Recommendations 🏆
   • Suggests items that similar users liked but the target user hasn't experienced yet.
4. Sorting by Interest Score 🔢
   • Recommendations are ranked based on the total ratings from similar users.

🛠️ Code Overview

# Create an instance of the recommender system
collab_recommender = CollaborativeFilteringRecommender()

# Get recommendations for Alice
recommendations = collab_recommender.recommend_items("Alice")

# Display recommendations
for title, score in recommendations:
    print(f"{title} | Estimated Interest Score: {score}")

🏷️ Example Users & Ratings

User	Movies & Songs Rated 🎶🎬
Alice	Inception (5), Interstellar (4), Bohemian Rhapsody (2)
Bob	The Dark Knight (5), Imagine (3), Inception (4)
Charlie	Avengers: Endgame (4), Hotel California (3)
David	The Matrix (5), Billie Jean (5)
Emma	Bohemian Rhapsody (5), Shape of You (4)

📢 Future Enhancements

🔹 Use cosine similarity for better user comparison 📊
🔹 Integrate a database for dynamic user ratings 💾
🔹 Implement a graphical user interface (GUI) for easy use 🎨
🔹 Support real-world datasets from platforms like IMDb & Spotify 🌍

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🎯 Get Started

🔹 Clone the repository
🔹 Run the Python script
🔹 Get personalized recommendations in seconds! 🚀

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👨‍💻 Developed as part of CodSoft Internship – Task 4 🎓
Feel free to contribute & improve the system! 💡