https://github.com/mnitin-reddy/collaborative-filtering-based-recommendation-system
This project is a Book Recommendation System that uses two main approaches: Popularity-Based and Collaborative Filtering. It recommends top books based on their rating frequency and average ratings, and also provides personalized book suggestions by analyzing user interactions.
https://github.com/mnitin-reddy/collaborative-filtering-based-recommendation-system
collaborative-filtering numpy pandas popularity-based-recommendation python recommendation-system scikit-learn
Last synced: about 1 month ago
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This project is a Book Recommendation System that uses two main approaches: Popularity-Based and Collaborative Filtering. It recommends top books based on their rating frequency and average ratings, and also provides personalized book suggestions by analyzing user interactions.
- Host: GitHub
- URL: https://github.com/mnitin-reddy/collaborative-filtering-based-recommendation-system
- Owner: MNitin-Reddy
- Created: 2024-10-09T06:25:58.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-08T09:54:07.000Z (5 months ago)
- Last Synced: 2025-02-08T11:44:03.826Z (3 months ago)
- Topics: collaborative-filtering, numpy, pandas, popularity-based-recommendation, python, recommendation-system, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 40.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Collaborative Filtering-Based Book Recommendation System
📚 **Project Overview**
This project implements a **Book Recommendation System** using a combination of:
- **Popularity-Based Recommendations** for suggesting the top 50 books based on aggregated ratings.
- **Collaborative Filtering** for personalized book recommendations tailored to individual users.
The system processes datasets containing books, users, and ratings to deliver meaningful recommendations. It is built with Python, employs ``cosine similarity`` for collaborative filtering, and is deployed as a web application using Flask.
---
## 💡 **Features**
- **Popularity-Based Recommendations**: Ranks books based on average rating and number of ratings to showcase the top 50 highly-rated books.
- **Personalized Recommendations**: Uses collaborative filtering to recommend books based on user preferences and similar users.
- **Efficient Data Processing**: Filters out invalid or sparse data to improve recommendation quality.
- **Flask Web Application**: Provides a user-friendly interface for browsing and receiving recommendations.
---
## 🔍 **Collaborative Filtering Explained**
Collaborative filtering is a machine learning technique used for making recommendations by finding similarities between users or items. In this project, we use **item-based collaborative filtering**, which focuses on identifying books similar to a given book based on user ratings.
### **Steps**
1. **Matrix Creation**: A pivot table (`pt`) is created where rows represent books, columns represent users, and values are ratings.
2. **Similarity Computation**: Cosine similarity is calculated between books to identify similar items.
3. **Recommendation Generation**: For a given book, top similar books are ranked and suggested to the user.
---
## 📂 **Datasets**
The system utilizes three datasets:
1. **Books Dataset (`Books.csv`)**:
Contains book information, including title, author, year, publisher, and cover image URLs.
- Example Columns: `ISBN`, `Book-Title`, `Book-Author`, `Year-Of-Publication`, `Publisher`
2. **Ratings Dataset (`Ratings.csv`)**:
Holds user ratings for books.
- Example Columns: `User-ID`, `ISBN`, `Book-Rating`
3. **Users Dataset (`Users.csv`)**:
Stores demographic details of users.
- Example Columns: `User-ID`, `Location`, `Age`
---
## 🚀 **Methodology**
### **1. Popularity-Based Recommendation System**
**Goal**: Recommend the top 50 books based on ratings and popularity.
**Steps**:
- Aggregate the number of ratings and average ratings for each book.
- Filter books with a significant number of ratings (e.g., >250 ratings).
- Rank and display the top 50 books with their titles, authors, ratings, and cover images.
---
### **2. Collaborative Filtering-Based System**
**Goal**: Generate personalized book recommendations.
**Steps**:
1. **User Filtering**: Select users with more than 200 ratings to ensure meaningful interactions.
2. **Book Filtering**: Retain books rated by at least 50 users for quality recommendations.
3. **Pivot Table**: Create a user-book rating matrix (`pt`).
4. **Similarity Calculation**: Compute cosine similarity between books.
5. **Recommendation**: Find and display the most similar books for a selected book.
---
### **3. Deployment**
- The system is deployed as a Flask web app.
- Recommendations are generated dynamically and displayed with book images, authors, and ratings.
---
## 🔧 **Technologies Used**
**Programming Languages**
- Python
**Libraries**
- **pandas**: Data manipulation
- **numpy**: Numerical computations
- **scikit-learn**: Cosine similarity
**Web Framework**
- **Flask**
**Data Visualization**
- **Bootstrap**: For UI design
**Serialization**
- **Pickle**: For efficient deployment of models and data
## 🖥️ How to Use
1. Clone the repository:
```bash
git clone
```
2. Navigate to the project directory:
```bash
cd book-recommender
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Flask application:
```bash
python app.py
```
5. Open your browser and go to `http://127.0.0.1:5000`.