Deep Learning 4 ay Lead AI Engineer & Architect

E-Commerce AI Recommendation Engine

Real-time, personalized product recommendation system for a large-scale e-commerce platform. A hybrid approach combining user behavior data and product features.

%42

Conversion Increase

<15ms

Average Latency

2M+

Daily Users

%28

Cart Value Increase

Challenge

Generating real-time recommendations with 2 million daily active users, solving the cold start problem, and maintaining recommendation diversity were the biggest challenges.

Solution

We designed a hybrid model architecture combining collaborative and content-based methods. Reduced latency below 15ms with Redis-based caching. Added a popularity-based fallback mechanism for new users.

Highlights

Hybrid recommendation model design and implementation

Real-time data streaming architecture (Kafka + Redis)

Continuous model improvement with A/B testing infrastructure

Smart fallback mechanism for cold start problem

Technology Stack

Python

PyTorch

FastAPI

Redis

Kafka

PostgreSQL

Docker

About the Project

In this project, we developed an end-to-end AI recommendation engine for an e-commerce platform with 2 million daily active users.

Technical Architecture

Collaborative Filtering for user similarity analysis

Content-Based Filtering for product feature matching

Deep Neural Network for hybrid model fusion

Redis-based real-time caching layer

Kafka for event-driven data streaming

Results

Within the first 3 months after going live, we observed a 42% increase in conversion rate and a 28% rise in average cart value.