📚 Book Recommendation System

A simple book recommendation system built using Streamlit and a similarity matrix to suggest books based on a user's input. The system uses a dataset of books and provides recommendations based on the most similar titles.

Hosted on Streamlit: https://personalizedreadingrecommendations-5ceie6kslmwvvk97zqeuzk.streamlit.app/

🚀 Features

• Book Recommendations: Users can input the title of a book, and the system will recommend similar books.
• Interactive Interface: Built using Streamlit, the app provides a clean, user-friendly interface for input and displaying recommendations.
• Real-Time Results: The app instantly displays recommendations based on the entered book title.
• Example Searches: Suggestions provided for sample book titles to help users start.

🎨 Technologies Used

• Streamlit: For the web interface.
• Pandas: To manage and manipulate the book dataset.
• Similarity Matrix: For calculating book similarities based on titles.
• Python: The core programming language used for the app.

🛠️ Installation

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ACSE-vg822/Personalized_Reading_Recommendations.git:
   cd Personalized_Reading_Recommendations

2. Create a virtual environment (Optional but recommended):

   python3 -m venv myenv
   source myenv/bin/activate   # On Windows use: myenv\Scripts\activate

3. Install the dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run streamlit_app.py

5. Navigate to your browser:

   The app will be locally hosted and can be found by following the link displayed in the terminal

📁 Folder Structure

/Personalized_Reading_Recommendations/
    ├── streamlit_app.py              # Main Streamlit app file
    ├── Data_Cleaning.ipynb           # Notebook for data cleaning
    ├── Model_and_evaluation.ipynb    # Notebook for training and evaluation
    ├── Utilities/
    │   ├── recommendation.py         # Contains the recommendation function
    │   ├── similarity_matrix.py      # Computing similarity matrix 
    ├── File_dump/
    │   ├── cosine_sim.pkl            # Pre-computed similarity matrix
    │   ├── df.pkl                    # Cleaned dataset of books
    ├── requirements.txt              # Project dependencies
    ├── static/
    │   ├── index_image.png           # Optional image for the background
    ├── README.md                     # Project README file

🔍 How to Use

1. Enter a Book Title: You can input any book title (e.g., Macbeth, Pride and Prejudice, Orlando).
2. View Recommendations: Based on your input, the app will provide a list of similar books.
3. Example Queries: If you're not sure what to search for, try one of the example queries under the input field.

⚡ Example Book Titles

• Macbeth
• Orlando
• Pride and Prejudice
• A Room of One's Own

🤔 Issues or Improvements?

Feel free to open an issue or create a pull request with improvements or bug fixes.