Overview
This recommendation system was built for an e-commerce platform aiming to provide personalized product suggestions to users based on their browsing and purchase history. The core goal was to enhance user engagement by delivering highly relevant recommendations, leading to a 30% increase in click-through rates and a 20% increase in sales.
Implementation Strategy
The system relied on a hybrid approach combining collaborative filtering with content-based filtering:
• Collaborative Filtering: The system recommended products based on users with similar behavior patterns.
• Content-Based Filtering: Analyzed the individual user's preferences, recommending products based on their unique interactions.
The recommendation engine was designed to process real-time user data and adapt the suggestions on the fly, ensuring fresh and relevant recommendations with each user session.
Technical Stack
• Python & SQL for data processing and analysis.
• TensorFlow for implementing the recommendation algorithms.
• React for the dynamic and responsive user interface.
Results
The system significantly improved user engagement metrics, leading to higher retention and a marked increase in average order value. Personalized recommendations provided more relevant product choices to users, driving both revenue and customer satisfaction.
