E-commerce Recommendation System in Java Swing
This project is a comprehensive E-commerce Recommendation System built using Java and Java Swing. Designed to enhance the user experience, it provides personalized product suggestions, cross-selling, upselling, and trending product recommendations through advanced algorithms.
Table of Contents
Introduction Project Structure Features Technologies Used Installation Usage Contributing License
Introduction
The Java Swing E-commerce Recommendation System is an interactive shopping platform that offers tailored recommendations, efficient sorting, and product search capabilities. Using pattern matching and binary search, it helps users find products quickly and accurately.
Project Structure
The project is organized as follows: EcommerceRecommendationSystem/ │ ├── .idea/ # IDE configuration files ├── EcommerceRecommendationSystem.iml # IntelliJ project config ├── .gitignore # Git ignore file ├── out/ # Compiled output files │ └── production/ ├── com/ │ └── example/ │ └── ecommerce/ │ ├── Dijkstra/ # Classes for Dijkstra's algorithm │ ├── Graph/ # Graph classes for recommendations │ ├── Main.java # Main class │ ├── MainFrame.java # Main application frame │ ├── PatternMatching.java # Pattern matching for search │ ├── Product.java # Product model │ ├── ProductPanel.java # Product display panel │ ├── RecommendationEngine.java # Recommendation logic │ ├── RecommendationPanel.java # Recommendation display panel │ ├── Searching.java # Search functionality │ ├── SearchPanel.java # Search panel for user input │ ├── Sorting.java # Product sorting utility │ ├── User.java # User model class │ ├── Resources/ │ │ └── welcomeimage.jpg # UI Welcome image │ └── WelcomePanel.java # Initial welcome screen panel └── External Libraries/ # Dependencies and libraries
Features
Product Panel: Displays all available products with details like name, price, rating, and popularity. Search Panel: Allows users to search for products using pattern matching and binary search. Recommendation Panel: Provides recommendations based on criteria such as: Best Sellers Related Products New Arrivals Trending Products Cross-Selling and Upselling Seasonal Discounts and Special Offers Algorithm Integration:
Utilizes Dijkstra's Algorithm for optimized recommendation paths. Implements Binary Search and Pattern Matching for efficient search operations.