ShopSphere AI
AI-Driven E-Commerce Platform with vector personalized recommendations.
Engineering Telemetry & Metrics
Bottlenecks & Scale Constraints
Platform bounce rate was 65% due to uncurated catalogs. Engineering costs were doubled by maintaining distinct codebases for Web and iOS.
Target Architecture & Execution
Consolidated onto a React Native monorepo, sharing over 90% of business logic. Engineered a vector-based recommendation engine using pgvector to power a personalized 'For You' discovery feed.
Key Architectural Pillars
Cross-Platform Monorepo
Vector-Based Recommendation
Unified Apollo Federation
Automated A/B Testing Pipelines
Data Pipeline & Node Flow
Unified GraphQL API serving Next.js and React Native. Rec Engine background worker updates user profiles in vector-enabled Postgres, calculating cosine similarity on-the-fly.
Mobile (RN Web)
Implementation Snippet
Performs K-Nearest Neighbors search in high-dimensional product embedding space. Weighs 'purchase' events higher than 'views' to compute personalized affinities.
def get_recommendations(user_id, history):
user_vector = vectorize(history, weights={'view': 1, 'cart': 3, 'buy': 5})
# KNN search in product embedding space
candidates = nearest_neighbors(user_vector, product_embeddings, k=100)
# Re-rank for inventory and margin
ranked = rank_by_business_logic(candidates)
return rankedDossier Specifications
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