Project Overview
This recommendation engine processes live telemetry streams from online storefronts. By deploying collaborative filtering algorithms alongside deep learning models in TensorFlow, it generates hyper-personalized product listings, upsell recommendations, and customized newsletters that boost average order values by up to 25%.
Key Features & Scope
Dynamic feed personalization updating within 150 milliseconds
Visual-similarity analysis suggesting items based on upload images
A/B testing dashboard for marketers to compare algorithm success rates
Background data worker pre-aggregating user purchase matrices
System Architecture
Python FastAPI acts as the ML inference server. Telemetry events flow into Redis Streams before processing. Front-end is rendered via Next.js with optimized static skeleton loading.