Recommender Systems Interview Questions
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DoorDash ML System Design: Multi-Channel Restaurant Recs
DoorDash ML design: build a multi-channel restaurant recommender with candidate gen & personalization, low-latency serving and channel-specific delivery.
eBay ML System Design: Post-Checkout Recommendations
Design a low-latency post-checkout recommendation system for eBay: retrieval, ranking, features, training, and metrics. Prepare for ML interviews with examples.
LinkedIn ML System Design: Real-Time Nearby Recommendations
Build a low-latency, scalable ML system to recommend nearby places in real time. Get architecture, dataflow, personalization tips, and interview follow-ups.
Microsoft ML System Design: Local Sports Team Recommender
Scalable recommender for local sports teams: data ingestion, candidate generation, ranking, real-time updates, and metrics. Prep for ML design interviews.
Palantir ML System Design: Scalable Music Recommender
Plan a scalable, low-latency music recommendation service for streaming platforms. Learn architecture, APIs, data models, and real-time updates for Palantir ML interviews.
Pinterest ML System Design: Real-Time Personalized Feed
Design a Pinterest-style real-time feed ranking system using embeddings, low-latency serving, and streaming events. Learn architecture choices and next steps.
Roblox ML System Design: Real-time Game Recommendations
Build a low-latency Roblox recommender that matches users to games using lifetime average playtime. Learn streaming aggregation, candidate generation and ranking.
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