https://github.com/taljindergill78/book-recommender-system
A personalized book recommender system that suggests books to users based on their reading preferences and past ratings, using collaborative filtering techniques on the Book Crossing dataset.
https://github.com/taljindergill78/book-recommender-system
big-data collaborative-filtering data-science jupyter-notebook kaggle-dataset machine-learning python recommendation-system
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A personalized book recommender system that suggests books to users based on their reading preferences and past ratings, using collaborative filtering techniques on the Book Crossing dataset.
- Host: GitHub
- URL: https://github.com/taljindergill78/book-recommender-system
- Owner: taljindergill78
- Created: 2025-06-06T04:51:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-06T05:11:28.000Z (about 1 year ago)
- Last Synced: 2025-07-11T06:21:09.986Z (about 1 year ago)
- Topics: big-data, collaborative-filtering, data-science, jupyter-notebook, kaggle-dataset, machine-learning, python, recommendation-system
- Language: Jupyter Notebook
- Homepage:
- Size: 21.5 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Book Recommender System
A personalized book recommender system that suggests books to users based on their reading preferences and past ratings, using collaborative filtering techniques on the Book Crossing dataset.
---
## 📂 Project Structure
```book-recommender-system/
│
├── Data/
│ ├── Books.csv
│ ├── Ratings.csv
│ ├── Users.csv
│
├── Notebooks/
│ ├── 01_data_preparation.ipynb
│ ├── 02_book_recommendation_engine.ipynb
│
├── Outputs/
│ ├── user_book_ratings.libsvm
│ ├── Top5_Book_Recommendations.csv
│
├── Reports/
│ ├── Book_Recommender_Report.docx
│
├── .gitignore
└── README.md
```
---
## 📘 Dataset Description
This project uses the [Book Crossing Dataset](https://www.kaggle.com/datasets/somnambwl/bookcrossing-dataset?resource=download) which includes:
- **Books.csv** – Metadata such as title, author, and ISBN
- **Users.csv** – User IDs and demographics
- **Ratings.csv** – User ratings of books on a 0–10 scale
Total:
📚 270,000+ books
👥 270,000+ users
⭐ 1 million+ ratings
---
## 🔍 Objective
To build a recommender system that:
- Understands user preferences from prior ratings
- Computes similarity between users using collaborative filtering
- Recommends top 5 books that a user hasn’t read yet but is likely to enjoy
---
## ⚙️ Methodology
1. **Data Cleaning & LIBSVM Preparation**
[`01_data_preparation.ipynb`](Notebooks/01_data_preparation.ipynb)
- Removes invalid entries
- Filters non-positive ratings
- Converts to sparse matrix and LIBSVM format
2. **Collaborative Filtering Engine**
[`02_book_recommendation_engine.ipynb`](Notebooks/02_book_recommendation_engine.ipynb)
- Builds user-user similarity matrix (Cosine similarity)
- Finds top 10 nearest neighbors per user
- Generates 5 book recommendations per user
- Outputs results to CSV
---
## 📁 Outputs
- `user_book_ratings.libsvm`: Sparse matrix in LIBSVM format
- `Top5_Book_Recommendations.csv`: Final output with recommended books and scores
---
## 🧾 Report
A clean summary of project objectives, methodology, and outcomes is available here:
[`Book_Recommender_Report.docx`](Reports/Book_Recommender_Report.docx)
---
## 🧑💻 Technologies Used
- Python (Pandas, NumPy, Scikit-learn, SciPy)
- Jupyter Notebooks
- Cosine Similarity (Collaborative Filtering)
- LIBSVM Format
---
## 👨🎓 Course
This academic project was developed under the course **IFT 511: Analyzing Big Data (Spring 2025)** at **Arizona State University**.
---
## 📝 Authors
Taljinder Singh