This Python script creates a book recommender system with a user-friendly interface using Streamlit, a Python library for building web applications. The system allows users to select a book from a dataset and receive recommendations based on the similarity of books in the dataset.
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Data Loading: The system loads a precomputed DataFrame containing book information and a similarity matrix calculated based on the book descriptions.
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Recommendation Algorithm: Upon selecting a book and clicking the "Recommend" button, the system calculates similarity scores between the selected book and others in the dataset. It then returns the top 5 recommended books based on these scores.
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User Interface: The user interface is designed using Streamlit and includes a title, a dropdown menu for selecting a book, and a button to trigger the recommendation process.
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Installation: Ensure you have Python installed along with the required dependencies:
pip install streamlit pandas
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Run the Application: Execute the following command to run the Streamlit application:
streamlit run app.py
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Select a Book: Choose a book from the dropdown menu displayed on the web interface.
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Get Recommendations: Click the "Recommend" button to view the top 5 recommended books based on the selected book's similarity scores.
The dataset used for this project contains a collection of books along with their genres and descriptions. It has been preprocessed and formatted for compatibility with the recommender system algorithm.
- This application is intended as a demonstration of how a simple book recommender system can be implemented using Streamlit.
- Users can explore its functionality and adapt it for their own datasets or integrate more advanced recommendation algorithms.
- The system's performance depends on the quality and size of the dataset as well as the accuracy of the similarity calculation algorithm.
- This project serves as a starting point for building more sophisticated recommendation systems and exploring user interaction in web applications.