Personalized Recommendation Engine
Deep learning-based recommendation system that increased user engagement by 35% and revenue by $2M.
Project Overview
Built a sophisticated recommendation engine using collaborative filtering and deep learning techniques to provide personalized product recommendations. The system processes user behavior data, product features, and contextual information to generate highly relevant suggestions.
The solution employs a hybrid approach combining matrix factorization, neural collaborative filtering, and content-based filtering. The model is trained on over 10 million user interactions and can handle cold-start problems for new users and products.
The recommendation engine serves over 1 million predictions daily with sub-100ms latency, integrated seamlessly into the company's e-commerce platform and mobile applications.
Key Features
- Real-time personalized recommendations with <100ms latency
- Cold-start handling for new users and products
- Multi-armed bandit testing for recommendation optimization
- Contextual recommendations based on time, location, and device
- Diversity and novelty controls to avoid filter bubbles
- A/B testing framework for continuous model improvement
- Explainable recommendations showing why items were suggested
Technologies Used
Project Gallery
Project Details
Client
Personal Project
Timeline
6 months (Q3 2022 - Q1 2023)
Role
Senior Data Scientist
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