Personalized recommendation system with collaborative filtering, content-based matching, and real-time ranking.
Shows personalized recommendations in feeds, carousels, and 'you may also like' sections.
Serves ranked recommendations with fallback to popularity-based when personalization is cold.
User-user and item-item similarity using matrix factorization (ALS) on interaction data.
TF-IDF and embedding similarity matching items by features, tags, and descriptions.
Final ranking combining multiple signals: relevance, freshness, diversity, and business rules.
Captures views, clicks, purchases, ratings for model training and real-time signals.
Pre-computed user and item features for low-latency serving.
Trained recommendation models with A/B test assignments and version history.
Explore this architecture with animated data flows, node auditing, and AI-powered analysis.
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