AI-Powered Product Recommendation Engine
Development of a personalized machine learning model to improve cross-selling and drive customer engagement on an e-commerce platform.
Architecture
Implemented a collaborative filtering model deployed via serverless functions, achieving a 12% lift in recommended product clicks.
User Management Module
Sales Pipeline View
Real-time Metrics
User Management Module
Custom Report Builder
Technologies include Python/Pandas, TensorFlow, and AWS Lambda for model deployment.
Key Results
- 12% increase in cross-sell conversion rate.
- Real-time recommendation generation (<50ms).
- Automated model retraining pipeline.
FAQ
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Utilized historical user interaction data (clicks, views, purchases) for training the initial model.
A method that makes predictions about a user's interests by collecting preferences from many users.