AI RECOMMENDATION ENGINE
predictive
E-Commerce AI Recommendation & Personalization Engine
A real-time hyper-personalized recommendation engine processing millions of clickstreams to deliver personalized product suggestions, dynamic pricing, and cross-sell promotions.
# Collaborative Filtering# Deep Learning# Redis# Kafka# TensorFlow
Contract formatContract development
Development system5 people
Development period4 months
Key Features
Core System Capabilities
Optimized modular design for both End-Users and Enterprise Management Admin
01
Shopper side
- Personalized home & product detail recommendations
- Frequently bought together bundles
- Real-time price drop & restock alerts
- Personalized search auto-complete
4 features
02
AI Recommendation Engine side
- Real-time clickstream event processing (Kafka)
- Graph collaborative filtering & matrix factorization
- Cold-start item recommendation model
- Sub-10ms Redis vector similarity lookup
4 features
03
Management side
- Recommendation strategy A/B testing
- Conversion uplift & CTR analytics
- Custom boosting & pin rule configurations
- Catalog sync API settings
4 features
Development Scope
Project Life Cycle & Phases
Phase 01
Requirement definition
Phase 02
UI/UX design
Phase 03
ML Pipeline engineering
Phase 04
Development
Phase 05
Unit / Integration testing
Phase 06
Operation and Maintenance
Tech Stack Architecture
Technologies Used in Project
Languages & Core Frameworks
Python, Java 21, Scala, TensorFlow
AI Tools & GenAI Models
Gemini Pro Personalization, DeepFM, Spark MLlib, Claude 3.5 Sonnet
Databases & Vector Storage
Redis Vector Search, Apache Cassandra, PostgreSQL
Infrastructure & Cloud Services
Apache Kafka, Spark Cluster, AWS EMR
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