https://github.com/duartium/booklu
Amazing book recommendation system project to apply design patterns and good programming practices.
https://github.com/duartium/booklu
angular api-rest best-practices csharp design-patterns dotnet-core tailwindcss typescript
Last synced: 12 months ago
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Amazing book recommendation system project to apply design patterns and good programming practices.
- Host: GitHub
- URL: https://github.com/duartium/booklu
- Owner: duartium
- License: mit
- Created: 2023-04-14T03:56:46.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-07T22:37:38.000Z (over 1 year ago)
- Last Synced: 2025-03-28T02:02:44.810Z (about 1 year ago)
- Topics: angular, api-rest, best-practices, csharp, design-patterns, dotnet-core, tailwindcss, typescript
- Language: C#
- Homepage: https://duartium.github.io/booklu/
- Size: 263 KB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📚 Booklu - Book Recommendations Engine 🚀
Welcome to Booklu, Book Recommendations Website! This is an open-source project that uses design patterns and best programming practices to create a dynamic and user-friendly book recommendations engine.
## Why BOOKLU?
Recommendation engines are a crucial part of the digital economy, driving personalized suggestions on platforms like Amazon, Netflix, and Spotify. However, finding personalized book recommendations can be a challenge. Our aim with BRE is to fill this gap and create an open-source book recommendations engine that is easy to implement and use.
## How does it work?
BRE uses a variety of algorithms and design patterns to provide personalized recommendations. We're implementing the Factory pattern, Singleton pattern among others, to ensure the code is easy to read, understand, and maintain.
## Dataset
The dataset used in this project is the [Amazon Books Reviews Dataset](https://www.kaggle.com/datasets/mohamedbakhet/amazon-books-reviews) obtained from Kaggle.
### Dataset Description
This dataset contains information about reviews for books sold on Amazon, including:
- Product ID (ASIN)
- Book Title
- Author
- Number of Reviews
- Average Ratings
- Review Date
The dataset contains over X million records, making it ideal for scalability and performance analysis in high-volume database systems.
### License
The dataset is available under the [insert license type if provided]. Please ensure that you follow the terms of the license when using this data.
### Source
- Dataset Name: **Amazon Books Reviews Dataset**
- Provider: Kaggle
- Link: [Amazon Books Reviews Dataset on Kaggle](https://www.kaggle.com/datasets/mohamedbakhet/amazon-books-reviews)
## Why should you contribute?
Contributing to BRE is a great opportunity to learn more about design patterns and best programming practices. In addition, you'll also get to:
- Work on an exciting, high-impact open-source project.
- Learn and practice Git and GitHub skills.
- Collaborate with other passionate developers.
- Improve your project portfolio.
- Help others discover their next favorite book!
## How to contribute
We love contributions from the community and are open to all kinds of contributions, from bug reports to new features. Please check out our [Contribution Guides](CONTRIBUTING.md) for more details on how you can contribute.
## Code of Conduct
To ensure an open and welcoming environment, we've adopted the [Contributor Covenant Code of Conduct](CODE_OF_CONDUCT.md). Please read and follow it in all your interactions with the project.
## Get started
To get started with working on Booklu, please follow the instructions in our [Installation Guide](INSTALLATION.md).
Thank you for your interest in Booklu! We look forward to your contributions. If you have any questions, feel free to [contact us](mailto:your-email@example.com).