Book Recommendation System

Overview

This publication showcases a Book Recommendation System designed to recommend books based on user interests using machine learning techniques.

Types of Recommendation Systems

1. Content-Based Filtering: Recommends items similar to what the user liked in the past.
2. Collaborative Filtering: Utilizes user interactions to suggest items based on what similar users liked.
3. Hybrid Systems: Combines both methods to enhance recommendation quality.

Dataset Used

The dataset for this project can be found here.

Model Implementation

This project uses the NearestNeighbors algorithm. The workflow includes:

1. Loading the data.
2. Initializing the value of k.
3. Calculating distances using Euclidean distance.
4. Sorting and selecting top k recommendations.

How to Run

Clone the repository:
git clone https://github.com/kattubadimohammad/Book-Recommendation-System

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Create and activate a conda environment:
conda create -n books python=3.7.10 -y conda activate books

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Install requirements:
pip install -r requirements.txt

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Run the application:
streamlit run app.py

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Author

K Mohammad
Data Scientist
Email: miraclemohammad786@gmail.com

External Resources

• Model Repository
• Kaggle Dataset

Methodology

The recommendation system employs a collaborative filtering approach, leveraging user interactions and preferences to provide personalized recommendations. By using the k-nearest neighbors algorithm, we calculate similarities between books based on user ratings and derive recommendations that enhance user engagement.

Results

The model successfully generates personalized book recommendations, demonstrating the efficacy of using user data to predict preferences. Initial evaluations show a high correlation between recommended books and user interests, with potential for further improvement through enhanced data inputs and advanced algorithms.

Key Features

• Personalized Recommendations: Tailors suggestions based on user preferences.
• Machine Learning Integration: Utilizes the k-nearest neighbors algorithm for effective similarity assessments.
• Interactive User Interface: Built with Streamlit for a seamless user experience.

Examples

• Suggests books from the same genre if a user enjoys a particular title.
• Provides a diverse range of recommendations by analyzing user interactions across different categories.

Conclusion

The Book Recommendation System leverages machine learning to deliver tailored book suggestions, significantly enhancing user engagement. Future iterations can benefit from expanded datasets and advanced algorithms to improve recommendation accuracy.