Designing a Scalable Personalized Learning Recommendation System
As learning platforms scale, users struggle to discover relevant content, leading to low engagement and poor completion rates.
This project explores how a personalized recommendation system can reduce decision friction and improve learning outcomes at scale.
What this demonstrates: • System design thinking (architecture, scalability) • Handling functional & non-functional requirements • Tradeoff evaluation across performance, personalization, and complexity
This document outlines the design of a personalized learning recommendation system aimed at improving course discovery and learning outcomes.
By increasing the relevance of content surfaced to users, the system seeks to reduce decision friction, improve engagement, and enable more effective learning journeys.
Target metrics are indicative and based on expected impact from improved personalization.