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📚 Book Recommendation System using LightFM

This project builds a hybrid book recommendation system using the LightFM library.
It leverages collaborative and content-based filtering to suggest books to users based on ratings data.

📦 Dataset

Source: Goodbooks-10K Dataset
Files used: books.csv, ratings.csv

🔧 Libraries

pandas, numpy, matplotlib
lightfm

🔮 Recommendation Logic

Trained a LightFM model using WARP loss function.
Built a user-item interaction matrix.
Predicted books that the user hasn't rated yet.
Displayed top-N recommendations per user.

📊 Visualization

Bar chart of top 10 most-rated books.

🚀 How to Run

pip install -r requirements.txt
jupyter notebook Book_Recommendation_LightFM.ipynb

✍️ Author
Hande Çarkcı


MİT licance
eğitim amaclı ve  Streamlit uygulamanda bu modeli kullandım

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