Building an E-Commerce Website with Product Recommendation System for Electronics Sales
Table of Contents
Introduction
In today’s digital age, e-commerce websites have become the backbone of retail, transforming the way consumers shop. A critical aspect of these platforms is providing a seamless user experience that not only retains customers but also drives sales. With the proliferation of online shopping, it has become increasingly important for e-commerce websites to deliver personalized experiences, such as recommending products that align with the preferences of individual users. This is where a product recommendation system comes into play, leveraging Machine Learning (ML) to enhance user engagement and satisfaction.
In this project, we aim to build an e-commerce website dedicated to electronics sales, incorporating a robust product recommendation system using a User-based Collaborative Filtering algorithm. This system will not only help users discover products tailored to their interests but also improve overall sales and website engagement.
The primary objective of this project is to develop an e-commerce website featuring a product recommendation system that enhances the shopping experience. By utilizing Machine Learning techniques, the website will be able to suggest relevant electronics products to users based on their browsing history, preferences, and behaviors. This personalized approach is designed to increase the likelihood of purchases, improve customer retention, and maximize advertising revenue.
E-Commerce Website with Product Recommendation System
Language Used
PHP5.6, PHP7.x
Database
MySQL 5.x
User Interface Design
HTML, AJAX,JQUERY,JAVASCRIPT
Web Browser
Mozilla, Google Chrome, IE8, OPERA
Software
XAMPP / Wamp / Mamp/ Lamp (anyone)
E-Commerce Website with Product Recommendation System
Product Recommendation System
At the heart of this project is the product recommendation system, which uses the User-based Collaborative Filtering algorithm. This method is highly effective in suggesting products that a user might like based on the preferences of similar users.
Steps in User-based Collaborative Filtering:
Similarity Calculation:
The system calculates the similarity between an active user and other users (neighbors) using the Euclidean Distance Similarity measure. This measure evaluates how closely related two users are based on their product ratings. The formula used is:
Once the similarity scores are calculated, the system selects a subset of users (neighborhood) who have the highest similarity scores to the active user. These users are likely to have similar tastes and preferences.
Prediction:
The system predicts the rating that the active user would give to a product based on the ratings provided by the selected neighbors. This prediction is used to recommend products to the user.
Recommendation:
Based on the predicted ratings, the system generates a list of recommended products that the active user is likely to purchase.
Step-by-Step Implementation
Running the Project
Download the project zip file.
Extract the file and copy the vehiclerecordsystem folder.
Paste inside the root directory:
For XAMPP: xampp/htdocs
For Wamp: wamp/www
For Lamp: var/www/Html
Open PHPMyAdmin:http://localhost/phpmyadmin
Create a database: Name it vrsdb
Import vrsdb.sql file: Located inside the SQL file folder in the zip package.
Run the script:http://localhost/vehiclerecordsystem
Data Collection:
Gather historical user data, including product ratings, purchase history, and browsing patterns.
Preprocessing:
Clean and preprocess the data to handle missing values, normalize ratings, and prepare it for similarity calculation.
Similarity Calculation:
Implement the Euclidean Distance Similarity formula to calculate the similarity between users.
Neighborhood Selection:
Identify and select a group of users who are most similar to the active user.
Prediction and Recommendation:
Predict the ratings for unrated products and generate personalized product recommendations.
Integration:
Integrate the recommendation engine into the e-commerce platform, ensuring seamless interaction with the user interface.
The integration of a product recommendation system in an e-commerce website for electronics sales is a powerful tool to enhance user experience and drive sales. By leveraging Machine Learning algorithms such as User-based Collaborative Filtering, the platform can deliver personalized product suggestions that align with the preferences of individual users. This not only increases the likelihood of purchases but also fosters customer loyalty and satisfaction.
