E-commerce Personalization Engine
Dealat City Recommendation Model
A machine learning recommendation system built for Dealat City — an e-commerce website and mobile app — delivering personalized product suggestions based on user behavior and purchase patterns.
01 — Model
ML-Powered Product Recommendations
A machine learning model trained on Dealat City user interactions — browsing history, cart activity, and purchases — to surface products each shopper is most likely to buy next.
- Collaborative and behavioral signals from real e-commerce activity
- Ranked product suggestions tuned for conversion and relevance
- Model trained and evaluated on Dealat City transaction data
- Scalable inference for website and mobile app traffic
ML-Powered Product Recommendations
Core capability powering this project.
02 — Personalization
Tailored Suggestions Per User
Recommendations adapt to individual shopper behavior rather than showing the same catalog to everyone — delivering a personalized browsing experience that reflects each user's interests and purchase history.
- Per-user ranking based on browsing and purchase patterns
- Cold-start handling for new users with limited history
- Dynamic updates as users interact with the platform
- Improved discovery of relevant products across categories
Tailored Suggestions Per User
Core capability powering this project.
03 — Integration
Deployed on Web & Mobile
The recommendation engine integrates into Dealat City's e-commerce website and mobile application — powering product carousels, "you may also like" sections, and personalized home feeds.
- API-ready serving layer for web and app frontends
- Recommendation slots across homepage, product pages, and checkout
- Low-latency responses for real-time shopper sessions
- Built for Dealat City's production e-commerce platform
Deployed on Web & Mobile
Core capability powering this project.