🎬 Hybrid Recommendation System

"Because Netflix isn't going to recommend itself..."

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Table of Contents

• About
• Model Details
• Dataset Used
• Approach
• How to Run
• Demo Screenshots / Video
• Features
• Contributing
• License

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About

Welcome to the Hybrid Recommendation System!
This project is a not-so-humble attempt to combine the best of collaborative and content-based filtering, because why settle for one when you can have both (and twice the bugs)?
Built with Python, Streamlit, and a healthy dose of sarcasm.

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Model Details

• Collaborative Filtering:
  Uses user-item interaction matrices to find users who are suspiciously similar to you (statistically, not emotionally).

  • Implemented with matrix factorization and nearest neighbors.
  • Handles cold start about as well as your average barista handles a complicated coffee order.

• Content-Based Filtering:
  Recommends movies based on their features (genres, tags, etc.), because sometimes you just want more of the same.

  • Uses TF-IDF and cosine similarity.
  • Ignores your actual taste, but hey, at least it’s explainable.

• Hybrid Model:
  Combines both approaches, so you get the worst of both worlds (just kidding, it’s actually pretty good).

  • Weighted blending of collaborative and content-based scores.
  • Tunable weights, because you love tweaking knobs.

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Dataset Used

• MovieLens (100k, 1M, or 10M, depending on how much RAM you want to sacrifice)
  • GroupLens MovieLens Datasets
  • Includes user ratings, movie metadata, and enough missing values to keep things interesting.

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Approach

1. Data Preprocessing:

   • Download and clean MovieLens data.
   • Encode user and item IDs (because models don’t like strings).
   • Engineer content features (genres, tags, etc.).
   • Split into train/test, because we’re not savages.

2. Model Training:

   • Train collaborative filtering model on user-item matrix.
   • Train content-based model on movie features.
   • Combine both in a hybrid model, because synergy.

3. Evaluation:

   • Precision, recall, F1, NDCG, RMSE, coverage, diversity, novelty, and other metrics you’ll probably ignore.
   • Interactive analytics dashboard, so you can pretend to care about the numbers.

4. Web Interface:

   • Built with Streamlit, because everyone loves sliders and buttons.
   • Real-time recommendations, analytics, and data exploration.
   • Sidebar navigation, because tabs are too mainstream.

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How to Run

1. Clone this repo

   git clone https://github.com/DevanshSrajput/hybrid-recommendation-system.git
   cd hybrid-recommendation-system
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Launch the web interface

   python main.py
   

   Or, if you’re feeling old-school:

   streamlit run src/web_interface.py
   

4. Profit
   (Just kidding, this is open source.)

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Demo Screenshots / Video

"A picture is worth a thousand words, but a screenshot is worth at least a bug report."

🏡Home Dashboard	📤 Recommendation Page
📊 Recommendation Preview	💬 Analytics
🤖 Analytics Preview	📊 Explore Data
⚙️ Data Preview	About

• WalkThrough Video:

Movie_Recommendation_System.mp4

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Features

• Hybrid recommendations (because two is better than one)
• Interactive Streamlit web UI
• Model evaluation and analytics
• Data exploration tools
• CLI mode for the terminally inclined
• Sarcastic comments (optional, but recommended)

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Contributing

Pull requests are welcome!
If you find a bug, congratulations—you’re now a contributor.
Open an issue or submit a PR.
Just don’t break the build (seriously).

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License

MIT License.
Because lawyers need jobs too.

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Made with ❤️ and a dash of cynicism by DevanshSrajput