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https://github.com/mani-prakash-n-r/book_recommendation_system_using_hybrid_technique
The Book Recommendation System uses collaborative and content filtering techniques to suggest books based on user preferences and content similarity.
https://github.com/mani-prakash-n-r/book_recommendation_system_using_hybrid_technique
csv html numpy pandas python sckiit-learn
Last synced: about 1 month ago
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The Book Recommendation System uses collaborative and content filtering techniques to suggest books based on user preferences and content similarity.
- Host: GitHub
- URL: https://github.com/mani-prakash-n-r/book_recommendation_system_using_hybrid_technique
- Owner: Mani-Prakash-N-R
- Created: 2024-12-04T06:13:48.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-23T12:38:59.000Z (about 2 months ago)
- Last Synced: 2024-12-23T13:31:54.479Z (about 2 months ago)
- Topics: csv, html, numpy, pandas, python, sckiit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 42.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📚 **Book Recommendation System Using Collaborative Filtering and Content Filtering Techniques** 🔍
## 🚀 **Overview**
The **Book Recommendation System** utilizes two powerful recommendation techniques: **Content-Based Filtering** and **Collaborative Filtering**. By combining these methods, the system provides personalized book recommendations to users based on their preferences and the collective behavior of other users.
### 🔑 **Key Features**
- **Content-Based Filtering** 📚: Recommends books based on similarities in book features (e.g., genre, author, description).
- **Collaborative Filtering** 🤝: Recommends books based on user behavior (e.g., ratings, reviews, purchase history) and similarities between users.
- **Hybrid Recommendation** 🔄: Combines both content-based and collaborative methods to improve recommendation accuracy.
---
## 🛠️ **Technologies Used**
- **Backend**: Python
- **Libraries/Frameworks**:
- `pandas`, `numpy`: For data processing and handling.
- `scikit-learn`: For machine learning tasks, such as similarity calculations.
- `surprise`: For collaborative filtering (user-based and item-based).
- `flask`: For web and API integration (optional).
- **Data**: User ratings, book details (title, genre, author, etc.)
---
## 🧑💻 **Installation**
### 📋 **Prerequisites**
Ensure you have the following installed:
- **Python 3.x**
- **pip** (Python package manager)
### 🔧 **Setup**
1. **Clone the Repository**:
```bash
git clone https://github.com/yourusername/book-recommendation-system.git
cd book-recommendation-system
2. **Install Required Dependencies**:
Install all necessary Python libraries by running the following command:
```bash
pip install -r requirements.txt
```
3. **Prepare Your Dataset**:
You will need a dataset containing:
- **Book Information**: Title, author, genre, description, etc.
- **User Interaction Data**: Ratings, reviews, purchase history, etc.
Ensure the dataset is in a suitable format (e.g., CSV or JSON) for the system to process.
---
## 🚀 **Running the Recommendation Engine**
1. **Run the Recommendation System**:
Start the recommendation engine with the following command:
```bash
python recommendation_system.py
```
The script will load your dataset, apply **Content-Based Filtering** and **Collaborative Filtering**, and output book recommendations.
2. **API Integration (Optional)**:
If you'd like to integrate this system with a web application, you can use **Flask** to run a simple API that exposes recommendation endpoints. To start the API server, run:
```bash
python app.py
```
The API will allow you to get book recommendations via HTTP requests. For example:
```bash
GET /recommendations?user_id=123
```
---
## 🔍 **Usage**
### 📖 **Content-Based Recommendations**:
The system recommends books similar to a given book based on its attributes, such as genre, author, or description.
```python
# Content-based recommendation for a specific book
book_recommendations = get_content_based_recommendations(book_id)
```
### 🤝 **Collaborative Filtering Recommendations**:
The system recommends books based on the behavior of similar users (e.g., users who rated similar books highly).
```python
# Collaborative filtering recommendation for a specific user
user_recommendations = get_collaborative_filtering_recommendations(user_id)
```
### 🔄 **Hybrid Model**:
The system combines both **Content-Based** and **Collaborative Filtering** techniques to provide more accurate recommendations.
---
## 📊 **Evaluation**
To assess the performance of the recommendation system, you can use the following metrics:
- **Precision**: Measures how many of the recommended books were liked by the user.
- **Recall**: Measures how many of the books the user liked were recommended.
- **RMSE (Root Mean Squared Error)**: For evaluating prediction accuracy in collaborative filtering models.
Example for calculating RMSE:
```python
from surprise import accuracy
# Evaluate RMSE of the collaborative filtering model
accuracy.rmse(predictions)
```
---
## 🌟 **Conclusion**
The **Book Recommendation System** combines the power of **Content-Based Filtering** and **Collaborative Filtering** to provide personalized book recommendations to users. By using a **hybrid model**, this system offers more accurate and diverse suggestions based on individual preferences and the collective behaviors of other users.
---
## ✨ **Key Takeaways**:
- AI-powered recommendations using **Content-Based Filtering** and **Collaborative Filtering**.
- **Hybrid recommendation model** for improved accuracy.
- **API integration** with Flask to serve recommendations in a web app.
## 📢 **Disclaimer**:
This system relies on **user data** (e.g., ratings and reviews) and **book details** (e.g., genre, title, description). Ensure that **data privacy** policies are followed when handling user information.
```