MangaMatch: Manga Recommendation Engine

Frontend: React
Backend: Express.js on Node.js
Database: MongoDB
State Management: Redux (planned for future updates)

MangaMatch is an recommendation engine aiming to help manga enthusiasts discover series tailored to their individual tastes.

Features

Multi-Manga Input: Allows users to input multiple manga titles they've enjoyed to generate more nuanced recommendations.
Advanced Recommendation Algorithm: Leverages one-hot encoding for streamlined genre categorization, and applies cosine similarity metrics for accurate manga-to-manga comparisons across multiple atributes like genres and themes.

MongoDB: Beyond just basic CRUD operations, I learned about data modeling and schema design, specifically the challenges of handling hierarchical data. I've also done some data cleaning through MongoDB to prepare the database for vectorization.
Express and API Development: Strengthened my understanding of RESTful APIs using Express.js, incorporating middleware for logging and error-handling.
MERN Stack: Gained experience in full-stack development by integrating MongoDB, Express.js, React, and Node.js. This project is an excellent showcase of frontend-backend data flow and state management.
Linear Algebra Applications: Applied cosine similarity to develop an advanced recommendation algorithm based on vectorized attributes.
Project Management: Navigated the complexities of building a nuanced feature set while keeping an eye on performance, usability, and code quality.

Clone the repository:

git clone https://github.com/csiv23/MangaMatch.git

Navigate to the project directory:
```
cd MangaMatch
```
Install dependencies:
```
npm install
cd client && npm install
```
Set up environment variables: Copy .env.sample to .env and populate the necessary variables.
Run the application:
```
npm run dev
```

Feedback, bug reports, and pull requests are welcome. Feel free to check issues for any upcoming features or bugs to tackle.