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- ## Hybrid Book Recommendation System
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-
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- This project is a Hybrid Book Recommender deployed on Hugging Face Spaces using Streamlit.
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- It combines Collaborative Filtering (ALS Matrix Factorization) with Semantic Search using Book Embeddings to generate intelligent recommendations.
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-
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- ## Features
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-
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- Search books using text queries (title/description similarity).
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-
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- Get personalized recommendations based on user embeddings.
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-
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- Hybrid scoring: CF + semantic similarity for better accuracy.
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-
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- Clean Streamlit UI for quick interaction.
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-
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- ## Project Structure
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- .
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- β”‚ app.py
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- β”‚ requirements.txt
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- β”‚ Dockerfile
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- β”œβ”€ als_user_factors.npy
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- β”œβ”€ als_item_factors.npy
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- β”œβ”€ item_embeddings.npy
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- β”œβ”€ meta_books.csv
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- β”œβ”€ user_to_index.pkl
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- β”œβ”€ item_to_index.pkl
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- └─ index_to_item.pkl
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-
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- ## Run Locally
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- ```bash
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- pip install -r requirements.txt
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- streamlit run app.py
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- ```
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-
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- ## Run with Docker
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-
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- docker build -t book-recommender .
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- docker run -p 7860:7860 book-recommender
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-
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- # Acknowledgments
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-
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- Implicit library for ALS
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-
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- SentenceTransformers for embeddings
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-
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- Streamlit for UI
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-
 
 
 
 
 
 
 
 
 
 
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  Hugging Face Spaces for hosting
 
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+ ---
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+ title: Book Recommender (Hybrid ALS + Semantic)
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+ emoji: πŸ“š
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+ colorFrom: blue
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+ colorTo: pink
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+ sdk: streamlit
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+ sdk_version: "1.35.0"
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+ app_file: app.py
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+ pinned: false
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+ ---
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+ ## Hybrid Book Recommendation System
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+
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+ This project is a Hybrid Book Recommender deployed on Hugging Face Spaces using Streamlit.
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+ It combines Collaborative Filtering (ALS Matrix Factorization) with Semantic Search using Book Embeddings to generate intelligent recommendations.
15
+
16
+ ## Features
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+
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+ Search books using text queries (title/description similarity).
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+
20
+ Get personalized recommendations based on user embeddings.
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+
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+ Hybrid scoring: CF + semantic similarity for better accuracy.
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+
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+ Clean Streamlit UI for quick interaction.
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+
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+ ## Project Structure
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+ .
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+ β”‚ app.py
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+ β”‚ requirements.txt
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+ β”‚ Dockerfile
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+ β”œβ”€ als_user_factors.npy
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+ β”œβ”€ als_item_factors.npy
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+ β”œβ”€ item_embeddings.npy
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+ β”œβ”€ meta_books.csv
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+ β”œβ”€ user_to_index.pkl
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+ β”œβ”€ item_to_index.pkl
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+ └─ index_to_item.pkl
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+
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+ ## Run Locally
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+ ```bash
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+ pip install -r requirements.txt
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+ streamlit run app.py
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+ ```
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+
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+ ## Run with Docker
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+
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+ docker build -t book-recommender .
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+ docker run -p 7860:7860 book-recommender
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+
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+ # Acknowledgments
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+
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+ Implicit library for ALS
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+
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+ SentenceTransformers for embeddings
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+
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+ Streamlit for UI
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+
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  Hugging Face Spaces for hosting