🎬 Mood-Aware Hybrid Movie Recommendation System

A hybrid movie recommendation system that suggests movies based on:

🎥 A movie you like 😊 Your current mood It combines content-based filtering with mood-based genre filtering and fetches real-time movie details using the TMDB API.

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🚀 Live Demo

https://mood-aware-hybrid-movie-recommendation-system-7cgsj8nqkbp8gaec.streamlit.app/

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📌 Problem Statement

This project improves recommendation quality by integrating mood-based filtering with content-based similarity to deliver more personalized and context-aware recommendations.

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📌 Project Overview

This project combines:

1. Content-Based Filtering

• CountVectorizer
• Cosine Similarity

2. Mood-Based Filtering

• Each mood mapped to specific genres
• Recommendations filtered based on mood

3. TMDB API Integration

• Fetches movie posters
• Ratings
• Release year
• Overview

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📊 Algorithms & Techniques Used

• CountVectorizer for text vectorization
• Cosine Similarity for content-based filtering
• Genre-to-Mood mapping logic
• API integration using Requests

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🛠 Tech Stack

• Python
• Streamlit
• Pandas
• Scikit-learn
• TMDB API
• Requests

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📂 Project Structure

Mood-Aware-Hybrid-Movie-Recommendation-System/
│
├── app.py
├── movies_dict.pkl
├── requirements.txt
├── README.md
│
└── .streamlit/
    └── secrets.toml

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##⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/Ankita7977/Mood-Aware-Hybrid-Movie-Recommendation-System.git

2️⃣ Navigate to the project folder

cd Mood-Aware-Hybrid-Movie-Recommendation-System

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Run the app

streamlit run app.py

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🔐 Environment Variables

This project requires a TMDB API key. ⚠️ Do not share your API key publicly.

Create a .streamlit/secrets.toml file and add:

TMDB_API_KEY = "your_api_key_here"

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⚙️ How It Works

1. User selects a movie.
2. System finds similar movies using cosine similarity.
3. Filters results based on selected mood.
4. Fetches movie details using TMDB API.
5. Displays top recommendations with posters and ratings.

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✨ Features

• 🎥 Movie-based recommendations
• 😊 Mood-based filtering (Happy, Sad, Excited, etc.)
• ⭐ Displays movie ratings
• 📅 Shows release year
• 🖼 Displays movie posters
• 📖 Shows movie overview
• ⚡ Fast and interactive user interface built with Streamlit

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🎯 Key Highlights

• Hybrid recommendation approach
• Mood-aware personalization
• Real-time API integration
• Clean and responsive UI

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🚀Future Improvements

• Add user authentication system
• Implement collaborative filtering
• Add user rating-based learning
• Improve UI/UX design
• Docker deployment
• Mobile responsive layout

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👩‍💻 Author

Ankita Prajapati
Aspiring Data Scientist | Machine Learning Enthusiast

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📸 App Preview

🏠 Home Screen

🎬 Recommendation Screen

📄 Detailed View