AI-Based Personalized Recommendation System for E-commerce
Project Overview
An AI-driven recommendation engine that enhances user shopping experience by suggesting products based on browsing history, preferences, and purchasing behavior.
We developed a cutting-edge recommendation engine for an e-commerce business to boost customer engagement and increase sales. The system analyzes user behavior, purchase history, and browsing patterns to suggest highly relevant products in real-time. The recommendations are displayed on product pages, the home page, and during the checkout process to drive upselling and cross-selling opportunities.
The Challenge
The client wanted to increase average order value and customer retention by delivering personalized shopping experiences. They needed an intelligent system capable of handling large datasets and providing fast, relevant suggestions without affecting site performance.
Our Solution
We implemented a machine learning-based recommendation system using collaborative filtering and content-based algorithms. The model continuously learns and updates based on new user data. A RESTful API was built for seamless integration with the client's existing e-commerce platform. The solution is scalable and optimized for real-time recommendations, ensuring both accuracy and performance.
Project Details
- Client:Confidential
- Date:October 2024
- Category:Artificial Intelligence
- Technologies:Python, TensorFlow, Scikit-Learn, Node.js, MongoDB
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