📝 REVIEWSAVER — Backend API

Authentication + Reviews + AI Recommendations
A secure, full-stack review platform built with Spring Boot, PostgreSQL, and a three-tier AI recommendation engine.

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🔗 Live Demo

Layer	URL
Frontend	authreview-reviewsaver-ari-cc660f.netlify.app
Backend API	Hosted on Render

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📖 Project Report

The full academic project report is available here:
📄 Project Report (GROUP-129).pdf

Submitted to VIT Bhopal University, School of Computing Science and Engineering, April 2026.

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📐 System Diagrams

Use Case Diagram

Illustrates all interactions between Guest users, Authenticated users, and the System.

Data Flow Diagram

Shows the authentication flow (JWT + OTP) and the review + AI recommendation pipeline.

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📋 Table of Contents

• About
• Tech Stack
• Features
• System Architecture
• AI Recommendation Engine
• API Endpoints
• Database Schema
• Getting Started
• Environment Variables
• Project Structure
• Performance
• Team

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🧠 About

REVIEWSAVER is a full-stack web application that solves a real problem: existing review platforms suffer from fake reviews, poor authentication, and generic recommendations. REVIEWSAVER addresses all three by combining:

• Multi-factor authentication (JWT + OTP via Gmail SMTP) to ensure only verified users submit reviews
• Full review management (CRUD, search, filtering, voting) across 5 categories
• A three-tier AI recommendation engine that personalizes suggestions based on user engagement level

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🛠 Tech Stack

Layer	Technology
Backend	Spring Boot 3.5.11, Spring Security, Spring Data JPA, Hibernate
Frontend	React 18.3, React Router 6.30
Database	PostgreSQL 17
Authentication	JWT (JSON Web Tokens), BCrypt, Gmail SMTP (OTP)
Deployment	Netlify (frontend), Render (backend), GitHub (version control)
Language	Java 17+ (backend), JavaScript ES6+ (frontend)

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✨ Features

🔐 Authentication & User Management

• OTP-based email verification on registration (6-digit, 10-min expiry)
• Stateless JWT token issuance on login
• BCrypt password hashing
• Password reset via OTP email flow
• Profile management (username, bio, location, DOB)

📝 Review Management

• Full CRUD operations on reviews
• 5 review categories: Electronics, Movies, Restaurants, Cafes, Food
• Upvote / Downvote system
• Keyword search, category filter, and multi-field sorting (date, rating, upvotes)
• Pagination support for large datasets
• Dashboard stats (total reviews, upvotes/downvotes received)

🤖 AI Recommendations

• Three-tier progressive recommendation engine (see below)
• "Find Your Perfect Match" mood-based wizard (multi-step preference quiz)
• Real-time ranked recommendations on the user dashboard
• Trending algorithm based on upvote counts and recency scoring

🔒 Security

• JWT authentication enforced on all protected routes
• CORS policy configuration
• 401 Unauthorized returned on all invalid/expired token attempts

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🏗 System Architecture

REVIEWSAVER follows a three-tier MVC architecture:

┌──────────────────────────────────────────────┐
│          Presentation Layer                  │
│    React 18.3 Frontend  →  Netlify           │
└─────────────────────┬────────────────────────┘
                      │ REST API (JSON)
┌─────────────────────▼────────────────────────┐
│          Application Logic Layer             │
│    Spring Boot 3.5.11 Backend  →  Render     │
│  ┌───────────┐ ┌──────────┐ ┌─────────────┐  │
│  │Auth Module│ │Review    │ │ AI Engine   │  │
│  │JWT + OTP  │ │CRUD Svc  │ │ 3-Tier Reco │  │
│  └───────────┘ └──────────┘ └─────────────┘  │
└─────────────────────┬────────────────────────┘
                      │ Spring Data JPA / Hibernate
┌─────────────────────▼────────────────────────┐
│              Data Layer                      │
│         PostgreSQL 17 (Cloud-hosted)         │
│   [Users Table] [Reviews Table] [Interactions Table] │
└──────────────────────────────────────────────┘

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🤖 AI Recommendation Engine

The recommendation engine adapts based on how much a user has interacted with the platform:

Tier	Trigger	Strategy
Tier 1	< 5 interactions	Category & keyword-based filtering
Tier 2	5–15 interactions	NLP-powered full-text analysis
Tier 3	15+ interactions	Collaborative filtering (interaction pattern matching)
Trending	All users	Upvote count + recency scoring

Users with more than 5 recorded interactions receive measurably more relevant recommendations — validated during performance testing.

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🌐 API Endpoints

Authentication

Method	Endpoint	Description	Auth Required
POST	/api/auth/register	Register new user	❌
POST	/api/auth/verify-otp	Verify OTP to activate account	❌
POST	/api/auth/login	Login, returns JWT	❌
POST	/api/auth/forgot-password	Send OTP for password reset	❌
POST	/api/auth/reset-password	Reset password with OTP	❌

Reviews

Method	Endpoint	Description	Auth Required
GET	/api/reviews	List all reviews (paginated, filterable)	❌
POST	/api/reviews	Create a new review	✅
PUT	/api/reviews/{id}	Update your review	✅
DELETE	/api/reviews/{id}	Delete your review	✅
POST	/api/reviews/{id}/upvote	Upvote a review	✅
POST	/api/reviews/{id}/downvote	Downvote a review	✅

Recommendations

Method	Endpoint	Description	Auth Required
GET	/api/recommendations	Get personalized recommendations	✅
POST	/api/recommendations/match	"Find Your Perfect Match" query	✅

User

Method	Endpoint	Description	Auth Required
GET	/api/user/profile	Get current user profile	✅
PUT	/api/user/profile	Update profile	✅
GET	/api/user/dashboard	Dashboard stats	✅

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🗄 Database Schema

Three core tables power the platform:

-- Users Table
users (id, email, password_hash, is_verified, otp, otp_expiry,
       username, phone, location, dob, bio, created_at)

-- Reviews Table  
reviews (id, product_name, category, content, rating,
         upvotes, downvotes, author_id [FK→users], created_at, updated_at)

-- Interactions Table
interactions (id, user_id [FK→users], review_id [FK→reviews],
              interaction_type [VIEW|VOTE|SUBMIT], created_at)

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🚀 Getting Started

Prerequisites

• Java 17+
• Maven 3.8+
• PostgreSQL 17
• Node.js 18+ (for frontend)

Backend Setup

# 1. Clone the repository
git clone https://github.com/RishiRaj1495/reviewsaver-backend.git
cd reviewsaver-backend

# 2. Configure environment variables (see below)
cp src/main/resources/application.properties.example src/main/resources/application.properties

# 3. Build and run
mvn clean install
mvn spring-boot:run

The backend will start at http://localhost:8080.

Frontend Setup

# Clone the frontend repo and install dependencies
git clone https://github.com/ari9516/reviewsaver-frontend.git
cd reviewsaver-frontend
npm install
npm start

The frontend will start at http://localhost:3000.

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⚙️ Environment Variables

Create src/main/resources/application.properties with the following:

# Database
spring.datasource.url=jdbc:postgresql://localhost:5432/reviewsaver
spring.datasource.username=YOUR_DB_USERNAME
spring.datasource.password=YOUR_DB_PASSWORD
spring.jpa.hibernate.ddl-auto=update

# JWT
jwt.secret=YOUR_JWT_SECRET_KEY
jwt.expiration=86400000

# Gmail SMTP (for OTP emails)
spring.mail.host=smtp.gmail.com
spring.mail.port=587
spring.mail.username=YOUR_GMAIL_ADDRESS
spring.mail.password=YOUR_GMAIL_APP_PASSWORD
spring.mail.properties.mail.smtp.auth=true
spring.mail.properties.mail.smtp.starttls.enable=true

# CORS
cors.allowed-origins=http://localhost:3000

⚠️ Never commit real credentials. Use environment variables or a secrets manager in production.

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📁 Project Structure

reviewsaver-backend/
├── src/
│   └── main/
│       ├── java/com/reviewsaver/
│       │   ├── auth/               # JWT, OTP, BCrypt logic
│       │   ├── user/               # User entity, service, controller
│       │   ├── review/             # Review CRUD, voting, search
│       │   ├── recommendation/     # 3-tier AI engine
│       │   ├── interaction/        # Interaction tracking
│       │   └── config/             # Security, CORS configuration
│       └── resources/
│           └── application.properties
├── docs/
│   ├── Project_Report_GROUP-129.pdf    ← Upload report here
│   └── diagrams/
│       ├── Use_Case_Diagram.png        ← Upload UCD image here
│       └── Data_Flow_Diagram.png       ← Upload DFD image here
├── pom.xml
└── README.md

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📊 Performance

Operation	Avg Response Time
Login / Register	< 150ms
Review CRUD	< 200ms
Search & Pagination	< 180ms
AI Recommendations	< 500ms
OTP Email Dispatch	2–3 seconds (Gmail SMTP)

Security validation confirmed: all unauthorized requests to protected endpoints return 401 Unauthorized.

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👥 Team

GROUP-129 — VIT Bhopal University, B.Tech CSE, 2028

Name	Roll Number	Roles
Rishi Raj	24BCE10149	Frontend Lead
Abhilash Singh	24BCE10706	UI/UX and System Design
Arnab Kumar	24BCE11017	Backend Lead + Database + API
Brotodeep Pal	24BCE10477	Security + Deployment

Project Guide: Dr. Sasmita Padhy, School of Computer Science and Engineering, VIT Bhopal University
Program Chair: Dr. Vikas Panthi

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📄 License

This project was developed as an academic submission for VIT Bhopal University. All rights reserved by the authors.