Lilly Tech Systems
AI Recommendation for E-commerce
Build intelligent product recommendation systems that drive revenue. Master collaborative filtering, content-based methods, hybrid approaches, and the placement strategies that maximize conversion and average order value.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
How product recommendations drive 35% of Amazon's revenue and why every e-commerce site needs AI-powered recommendations.
2. Collaborative Filtering
User-based and item-based collaborative filtering, matrix factorization, and implicit feedback models for product recommendations.
3. Content-Based Methods
Product attribute matching, NLP for product descriptions, visual similarity, and knowledge graph-based recommendations.
4. Hybrid Models
Combining collaborative and content-based approaches, deep learning recommenders, and two-tower architectures.
5. Placement Strategy
Where and when to show recommendations: PDP, cart, homepage, email, and post-purchase for maximum revenue impact.
6. Measurement
A/B testing recommendations, revenue attribution, beyond-accuracy metrics, and continuous model improvement.
What You'll Learn
By the end of this course, you'll be able to:
Build Recommenders
Create production-ready recommendation systems using collaborative filtering, content-based, and hybrid approaches.
Increase Revenue
Optimize recommendation placements and algorithms to maximize conversion rate and average order value.
Measure Impact
Design rigorous A/B tests and track beyond-accuracy metrics that capture the true business value of recommendations.
Scale Systems
Architect recommendation systems that handle millions of products and users with real-time inference at scale.