Project Overview
This project implements a sophisticated recommendation system using collaborative filtering and content-based filtering techniques. The system provides personalized product recommendations to users based on their browsing history, purchase patterns, and product preferences.
Project Vision
The goal was to create an intelligent recommendation engine that could enhance user experience and increase conversion rates for an e-commerce platform. The system needed to handle cold-start problems and provide real-time recommendations.
Implementation Details
The project combines multiple recommendation approaches to create a hybrid system. Key features include:
- Collaborative filtering with matrix factorization
- Content-based filtering using product attributes
- Real-time recommendation updates
- A/B testing framework
- Scalable architecture for large datasets
Results and Impact
The recommendation system significantly improved user engagement and sales metrics:
- 40% increase in click-through rates
- 25% increase in conversion rates
- 15% increase in average order value
- Reduced bounce rates by 30%