Design an e-commerce recommendation system
AmazonSoftware Engineer · Technical Screen
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This question evaluates expertise in large-scale machine learning system design, covering competencies in recommendation algorithms, candidate generation and ranking, feature engineering and stores, low-latency serving, and operational reliability; it is categorized in the ML system design domain and requires both conceptual understanding and practical application. It is commonly asked to assess the ability to balance personalization and business metrics (CTR, CVR, revenue), address scalability and latency targets, and manage data quality, cold-start, exploration–exploitation, A/B testing, bias/fairness, and monitoring trade-offs in production.
Design an Amazon-scale e-commerce product recommendation system. Specify the primary use cases (home feed, product detail, cart, email) and success metrics (CTR, conversion, revenue, diversity, freshness). Propose an architecture with candidate generation and ranking services, including key features (user behavior, item content, graph/co-view signals), model choices (MF, deep retrieval, sequence models, gradient-boosted trees), and a feature store. Detail offline training pipelines, near-real-time updates, online serving, caching, latency/SLA targets, and scale estimates. Address cold start for users/items, long-tail coverage, exploration–exploitation (e.g., bandits), and A/B testing design and guardrails. Discuss data quality, feedback loops, bias/fairness, privacy, abuse prevention, monitoring, and rollback plans.
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