Book Recommendation System

A recommendation system built using the Funk SVD model to suggest books based on user ratings.

Overview

• Model: Funk SVD
• Dataset: Book Recommendation Dataset from Kaggle
• Key Metrics: RMSE (achieved ~3.455943 in tests)

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.x
• Streamlit
• Surprise library
• PyYAML

Dataset and Model Setup

Due to the large size of the dataset and model files:

1. Download the dataset files from the Kaggle link and place them inside a folder named dataset in the project directory.

2. Before running the Streamlit app, execute the main.ipynb notebook. This will generate the necessary model files. Note: You can skip the hyperparameter tuning process in the notebook as it is time-consuming.

Running the Model

1. Clone the repository:

bash

git clone https://github.com/haranobuhardo/surprise-svd-book-recommendation-system

2. Navigate to the project directory:

bash

cd surprise-svd-book-recommendation-system

3. Run the Streamlit app:

bash

streamlit run app.py

This will launch a web application where you can test the recommendation system.

Deployment

The model can be deployed using Streamlit for real-time book recommendations. Ensure you have Streamlit installed and simply run the provided app.py to start the server and interact with the model.

Report

For a detailed report on the project, check out the article: Recommendation System: Harnessing Machine Learning for Enhanced Book Recommendations (Medium)