🎬 Context-Aware Multilingual Movie Recommender — Extended

🧠 Extended Edition — Multi-language, Context-aware, Explainable, and Production-friendly

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📖 Overview

This project is an AI-powered movie recommendation system designed to provide personalized, multilingual, and context-aware movie suggestions.
It enhances the traditional recommendation pipeline by incorporating user mood, time of day, weather, and preferred language, enabling a more human-like recommendation experience.

Built using Streamlit, Python, and XGBoost, the system combines Collaborative Filtering, Content-based Similarity, and Machine Learning approaches to deliver explainable and diverse recommendations.

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🚀 Features

• 🌐 Multilingual Support: Handles movies in Hindi, Telugu, Malayalam, Kannada, and more.
• 😃 Context-Aware Recommendations: Takes user mood, time, and weather into account.
• 🧩 Multiple Strategies:
  • Top Rated Movies
  • Content-Based Similarity
• 🧠 Explainable ML: (optional) Uses XGBoost for per-movie “like/dislike” predictions.
• 📊 Interactive Dashboard: Displays dataset statistics and analytics.
• 🛠️ Streamlit UI: Fast, lightweight, and user-friendly web interface.

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🏗️ Project Structure

Recommendator_Movies/ │ ├── app.py # Main Streamlit Application ├── generate_movies.py # Generates or loads multilingual movie dataset ├── generate_ratings.py # Simulates or processes user ratings ├── movies.dat # Movie metadata (movieId, title, genres, language) ├── ratings.dat # User ratings (userId, movieId, rating, timestamp) ├── users.dat # User profiles (userId, gender, age, occupation, location) ├── requirements.txt # Required Python dependencies └── README.md # Project documentation

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⚙️ Installation

1️⃣ Clone the repository

git clone https://github.com/<your-username>/Recommendator_Movies.git
cd Recommendator_Movies

2️⃣ Create a virtual environment
python3 -m venv venv
source venv/bin/activate     # For Mac/Linux
venv\Scripts\activate        # For Windows

3️⃣ Install dependencies
pip install -r requirements.txt

4️⃣ Run the app
streamlit run app.py

🧩 How It Works

Dataset Loading:
Reads movie, user, and rating data (movies.dat, ratings.dat, users.dat).

Feature Engineering:
Extracts genres, encodes categorical features like language, mood, time, and weather.

Training:
Uses XGBoost for binary classification (like/dislike) based on contextual features.

Recommendation:
Top Rated: Ranks by average rating.
Content-Based: Uses genre and language similarity.

Visualization:
Displays data insights — total movies, ratings, users, and average rating via charts.

📊 Example Dashboard
Dataset Overview Section:
Total Movies: 60,000
Total Ratings: 595,938
Unique Users: 5,000
Avg Rating: 3.46

Charts:
Movies per Language
Ratings Distribution
Top Genres by Popularity

📚 Technologies Used

Category	Tools
Programming	Python 3.x
ML Model	XGBoost
Web Framework	Streamlit
Libraries	pandas, numpy, sklearn, matplotlib, seaborn
Visualization	Plotly / Altair
Data Format	.dat (MovieLens-style structured data)

🧑‍💻 Example Commands
To re-train ML model:
python app.py --train
To launch Streamlit dashboard:
streamlit run app.py

To update data:
python generate_movies.py
python generate_ratings.py

🧠 Future Improvements
Integration with real-world APIs (IMDb, TMDB)
Deep learning embeddings (transformers)
Sentiment-aware review analysis
Enhanced explainability with SHAP